Initial commit

model.properties

+# 模型唯一标识
+modelCode=1452
+# 模型名称
+modelName=olmocr_pytorch
+# 模型描述
+modelDescription=Ai2开发的基于阿里千问多模态OCR模型
+# 应用场景
+appScenario=推理,OCR,电商,教育,广媒,交通，政府
+# 框架类型
+frameType=pytorch

olmocr/__init__.py

+from .version import VERSION, VERSION_SHORT

olmocr/bench/README.md

+# olmOCR-Bench
+
+We develop olmOCR-Bench in order to automatically and effectively evaluate document-level
+parsing and OCR of various tools.
+
+olmOCR-Bench works by testing various "facts" or "properties" about document pages at the PDF-level.
+We choose PDFs directly, because PDFs do preserve some digital metadata and information which is helpful
+and commonly available. Almost any other format can be converted to a PDF, but not the reverse.
+
+## Property classes
+
+- Text presence/absence
+ - This task makes sure that a given small piece of text (ex. 1-3 sentence level) is present with high probability within
+    a parsed document. It looks at documents with ambiguity around headers, footers, and other ambiguous content. Text still
+    has a fuzzy matching allowed.
+- Natural Reading Order
+ - This task ensures that blocks of text which are present have a defined order relative to one another. For example,
+  on a document that contains multiple news articles on one page, you'd want to see that the first sentence of the 
+  first article appears after the heading of that article. But, you may be okay with swapping the order of those 
+  two articles.
+- Table Accuracy
+ - Pages with tables get parsed out and are checked for accuracy on a direct row/column/title basis.
+- Formula Accuracy
+ - Extract formula from document, render it, and compare rendering using foundation model.
+
+Table Format:
+ - pdf_filename
+ - Task ID
+ - Type: text_presence, text_absense, reading_order, table
+ - text_presence, text_absense: {text: str, fuzzy_threshold: float}
+ - reading_order: {target_text_presence: task_id, appears_before: task_id, appears_after: task_id}
+ - table: {table_index: int, needs to be fuzzy as well, ex. does row exist with column text X, does column exist with a row containing Y}
+ - formula: TODO
+
+## Creation
+
+We sampled documents from the same source as olmocrmix. We run them through two models, and see which ones have biggest 
+plain textual diffs, but still contain lots of good text, and aren't just tables/formula heavy for now.
+Then, we will extract text presence/absense markers and verify using tinyhost UI. 
+Write those to JSON. Maybe do some embedding and grouping to try to get lots of variation, at least when 
+prioritizing manual review.
+
+Later, we will repeat the same for tables and formulas.
+
+Write the evalutor script which will output a nice templated tinyhostable results page.
+
+## Running
+We do not want to depend on a model having any specific format of its output.
+
+Step 1. Download dataset with all pdfs (all will be single page) to /pdfs
+Step 2. Run your extraction on it, point output to folder, ex. olmocr-v2_1/ where you expect pdf_page1.md for /pdfs/pdf_page1.pdf file
+Step 3. Run the evaluation script
+Step 4. Get results, and use tinyhost to view all failing examples
+
+### Running existing scripts
+
+```bash
+pip install marker-pdf==1.5.4
+python olmocr/bench/runners/run_marker.py olmocr/bench/sample_data/pdfs
+
+pip install verovio torchvision
+python olmocr/bench/runners/run_gotocr.py olmocr/bench/sample_data/pdfs
+
+conda create -n MinerU python=3.10
+conda activate MinerU
+pip install -U magic-pdf[full]==1.1.0 --extra-index-url https://wheels.myhloli.com
+pip install huggingface_hub
+wget https://github.com/opendatalab/MinerU/raw/master/scripts/download_models_hf.py -O download_models_hf.py
+python download_models_hf.py
+python olmocr/bench/runners/run_mineru.py olmocr/bench/sample_data/pdfs
+```

olmocr/bench/benchmark.py

+#!/usr/bin/env python3
+"""
+This script runs olmocr bench.
+It will take as an argument a folder, and scan it for .jsonl files which contain the various rules and properties that we will check.
+It will then validate the JSON files to make sure they are all valid.
+Then, each other folder in there (besides /pdfs) represents a pipeline tool that we will evaluate.
+We will validate that each one of those contains at least one .md file (or repeated generations, e.g. _pg{page}_repeat{repeat}.md)
+corresponding to its parse for every .pdf in the /pdfs folder.
+Then, we will read each one, and check if they pass against all the rules.
+If a rule fails on some of the repeats, a short explanation is printed.
+The final score is averaged over the repeated generations.
+Statistical analysis including bootstrap confidence intervals are provided for the results.
+Pairwise permutation tests are conducted between specific candidate pairs.
+"""
+
+import argparse
+import glob
+import os
+import sys
+import re
+
+from typing import Dict, List, Tuple, Optional
+from pypdf import PdfReader
+
+from .tests import BasePDFTest, BaselineTest, load_tests
+from .katex.render import clear_cache_dir
+from .utils import calculate_bootstrap_ci, perform_permutation_test
+
+def evaluate_candidate(
+    candidate_folder: str, all_tests: List[BasePDFTest], pdf_basenames: List[str], force: bool=False
+) -> Tuple[float, int, List[str], List[str], Dict[str, List[float]], List[float]]:
+    """
+    For the candidate folder (pipeline tool output), validate that it contains at least one .md file
+    (i.e. repeated generations like _pg{page}_repeat{repeat}.md) for every PDF in the pdf folder.
+    Then, run each rule against all corresponding .md files and average the results.
+
+    Returns a tuple:
+      (overall_score, total_tests, candidate_errors, test_failures, test_type_breakdown, all_test_scores)
+
+      - overall_score: Average fraction of tests passed (averaged over repeats and tests).
+      - total_tests: Total number of tests evaluated.
+      - candidate_errors: List of candidate errors (e.g. missing files).
+      - test_failures: List of failure messages for tests not passing on all repeats.
+      - test_type_breakdown: Dictionary mapping test type to list of average pass ratios for tests of that type.
+      - all_test_scores: List of all individual test scores (used for bootstrapping).
+    """
+    candidate_errors = []
+    test_failures = []
+    test_type_breakdown = {}  # key: test type, value: list of average pass ratios
+    all_test_scores = []  # Store all individual test scores for bootstrapping
+    candidate_name = os.path.basename(candidate_folder)
+
+    # Map each PDF to its corresponding MD repeats (e.g., doc1_pg1_repeat1.md, doc1_pg2_repeat2.md, etc.)
+    pdf_to_md_files = {}
+    for pdf_name in pdf_basenames:
+        md_base = os.path.splitext(pdf_name)[0]
+        # Updated regex for new format: {pdf_name}_pg<page>_repeat<repeat>.md
+        md_regex = re.compile(rf"^{re.escape(md_base)}_pg\d+_repeat\d+\.md$")
+        
+        # List all files in the candidate folder and filter using regex
+        all_files = os.listdir(candidate_folder)
+        md_files = [os.path.join(candidate_folder, f) for f in all_files if md_regex.match(f)]
+        
+        if not md_files and not force:
+            candidate_errors.append(
+                f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} "
+                f"(expected files matching {md_base}_pg{{page}}_repeat*.md)."
+            )
+        else:
+            pdf_to_md_files[pdf_name] = md_files
+
+    if candidate_errors:
+        return (0.0, len(all_tests), candidate_errors, test_failures, test_type_breakdown, all_test_scores)
+
+    total_test_score = 0.0
+
+    # Evaluate each test. Each test references a PDF (e.g., "doc1.pdf") and a specific page.
+    for test in all_tests:
+        test_type = test.type
+        if test_type not in test_type_breakdown:
+            test_type_breakdown[test_type] = []
+        pdf_name = test.pdf
+        md_base = os.path.splitext(pdf_name)[0]
+        md_files = pdf_to_md_files.get(pdf_name, [])
+        # Filter MD files for the specific page corresponding to the test
+        page_md_files = [f for f in md_files if re.search(rf"_pg{test.page}_", os.path.basename(f))]
+        if not page_md_files:
+            candidate_errors.append(
+                f"Candidate '{candidate_name}' is missing MD repeats for {pdf_name} page {test.page} "
+                f"(expected files matching {md_base}_pg{test.page}_repeat*.md)."
+            )
+            continue
+
+        repeat_passes = 0
+        num_repeats = 0
+        explanations = []
+        for md_path in page_md_files:
+            num_repeats += 1
+            try:
+                with open(md_path, "r", encoding="utf-8") as f:
+                    md_content = f.read()
+            except Exception as e:
+                candidate_errors.append(f"Error reading {md_path}: {e}")
+                continue
+
+            try:
+                # Use the test's run method to evaluate the content
+                passed, explanation = test.run(md_content)
+                if passed:
+                    repeat_passes += 1
+                else:
+                    explanations.append(explanation)
+            except Exception as e:
+                candidate_errors.append(f"Error running test {test.id} on {md_path}: {e}")
+                explanations.append(str(e))
+
+        test_avg = repeat_passes / num_repeats if num_repeats > 0 else 0.0
+        all_test_scores.append(test_avg)  # Add to list for bootstrapping
+        total_test_score += test_avg
+        if test_avg < 1.0:
+            test_failures.append(
+                f"Test {test.id} on {md_base} page {test.page} average pass ratio: {test_avg:.3f} "
+                f"({repeat_passes}/{num_repeats} repeats passed). Ex: {explanations[0] if explanations else 'No explanation'}"
+            )
+        test_type_breakdown[test_type].append(test_avg)
+
+    overall_score = total_test_score / len(all_tests) if all_tests else 0.0
+    return (overall_score, len(all_tests), candidate_errors, test_failures, test_type_breakdown, all_test_scores)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Run OLMOCR Bench.")
+    parser.add_argument(
+        "--input_folder",
+        default=os.path.join(os.path.dirname(__file__), "sample_data"),
+        help="Path to the folder containing .jsonl files, /pdfs folder, and pipeline tool subfolders.",
+    )
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="Run benchmark even if some files are missing",
+    )
+    parser.add_argument(
+        "--candidate",
+        type=str,
+        default=None,
+        help="Run test only for a single candidate"
+    )
+    parser.add_argument(
+        "--bootstrap_samples",
+        type=int,
+        default=1000,
+        help="Number of bootstrap samples for confidence interval calculation (default: 1000).",
+    )
+    parser.add_argument(
+        "--confidence_level",
+        type=float,
+        default=0.95,
+        help="Confidence level for interval calculation (default: 0.95 for 95% CI).",
+    )
+    parser.add_argument(
+        "--permutation_tests",
+        action="store_true",
+        help="Run permutation testing",
+    )
+    args = parser.parse_args()
+
+    input_folder = args.input_folder
+    n_bootstrap = args.bootstrap_samples
+    ci_level = args.confidence_level
+    pdf_folder = os.path.join(input_folder, "pdfs")
+
+    # Check that the pdfs folder exists
+    if not os.path.exists(pdf_folder):
+        print("Error: /pdfs folder must exist in your data directory.", file=sys.stderr)
+        sys.exit(1)
+
+    # Find all pdf files in the pdf folder
+    all_pdf_files = list(glob.glob(os.path.join(pdf_folder, "*.pdf")))
+    if not all_pdf_files:
+        print(f"Error: No PDF files found in {pdf_folder}", file=sys.stderr)
+        sys.exit(1)
+
+    # Get PDF basenames (e.g. "doc1.pdf")
+    pdf_basenames = [os.path.basename(p) for p in all_pdf_files]
+
+    # Find and validate .jsonl files in the input folder
+    jsonl_files = glob.glob(os.path.join(input_folder, "*.jsonl"))
+    if not jsonl_files:
+        print(f"Error: No .jsonl files found in {input_folder}.", file=sys.stderr)
+        sys.exit(1)
+
+    # Load and concatenate all test rules from JSONL files
+    all_tests = []
+    for jsonl_path in jsonl_files:
+        tests = load_tests(jsonl_path)
+        all_tests.extend(tests)
+
+    if not all_tests:
+        print("No valid tests found. Exiting.", file=sys.stderr)
+        sys.exit(1)
+
+    # Add in a default repeat test for every PDF that doesn't already have one
+    for pdf in pdf_basenames:
+        if not any(t.type == "baseline" for t in all_tests if t.pdf == pdf):
+            all_tests.append(BaselineTest(id=f"{pdf}_baseline", pdf=pdf, page=1, type="baseline"))
+
+    # Make sure that each PDF and page has at least one test in it
+    for pdf in pdf_basenames:
+        pdf_doc = PdfReader(os.path.join(pdf_folder, pdf))
+        
+        for page in range(1, len(pdf_doc.pages) + 1):
+            if not any(test for test in all_tests if test.pdf == pdf and test.page == page) and not args.force:
+                print(f"No dataset entry found for pdf {pdf} page {page}")
+                sys.exit(1)
+
+    # Identify candidate pipeline folders (subdirectories of input_folder excluding /pdfs)
+    candidate_folders = []
+    for entry in os.listdir(input_folder):
+        full_path = os.path.join(input_folder, entry)
+        if args.candidate is not None:
+            if entry == args.candidate:
+                candidate_folders.append(full_path) 
+        else:
+            if os.path.isdir(full_path) and entry != "pdfs":
+                candidate_folders.append(full_path)
+
+    if not candidate_folders:
+        print("Error: No candidate pipeline folders found (subdirectories besides 'pdfs').", file=sys.stderr)
+        sys.exit(1)
+
+    candidate_folders.sort()
+
+    # Evaluate each candidate
+    summary = []
+    print("\nRunning tests for each candidate:")
+    for candidate in candidate_folders:
+        candidate_name = os.path.basename(candidate)
+        overall_score, total_tests, candidate_errors, test_failures, test_type_breakdown, all_test_scores = evaluate_candidate(
+            candidate, all_tests, pdf_basenames, args.force,
+        )
+        
+        # Calculate confidence interval
+        if all_test_scores:
+            ci = calculate_bootstrap_ci(all_test_scores, n_bootstrap=n_bootstrap, ci_level=ci_level)
+        else:
+            ci = (0.0, 0.0)
+            
+        summary.append((candidate_name, overall_score, total_tests, candidate_errors, test_failures, test_type_breakdown, ci, all_test_scores))
+        
+        print(f"\nCandidate: {candidate_name}")
+        if candidate_errors:
+            for err in candidate_errors:
+                print(f"  [ERROR] {err}")
+        else:
+            if test_failures:
+                for fail in test_failures:
+                    print(f"  [FAIL] {fail}")
+            print(f"  Average Score: {overall_score * 100:.1f}% (95% CI: [{ci[0] * 100:.1f}%, {ci[1] * 100:.1f}%]) over {total_tests} tests.")
+
+    # Print final summary with breakdown by test type
+    print("\n" + "=" * 60)
+    print("Final Summary with 95% Confidence Intervals:")
+    for candidate_name, overall_score, total_tests, candidate_errors, _, test_type_breakdown, ci, _ in summary:
+        if candidate_errors:
+            status = "FAILED (errors)"
+            ci_str = "N/A"
+            ciw_str = ""
+        else:
+            status = f"{overall_score * 100:0.1f}%"
+            half_width = ((ci[1] - ci[0]) / 2) * 100
+            ciw_str = f"± {half_width:0.1f}%"
+            ci_str = f"[{ci[0] * 100:0.1f}%, {ci[1] * 100:0.1f}%]"
+        
+        print(f"{candidate_name:20s} : Average Score: {status} {ciw_str}")
+        
+        for ttype, scores in test_type_breakdown.items():
+            if scores:
+                avg = sum(scores) / len(scores) * 100
+            else:
+                avg = 0.0
+            
+            print(f"    {ttype:8s}: {avg:0.1f}% average pass rate over {len(scores)} tests")
+        
+        print("")
+    
+    # Perform pairwise permutation tests
+    if args.permutation_tests:
+        print("\n" + "=" * 60)
+        print("Pairwise Permutation Tests:")
+        
+        valid_candidates = [c for c in summary if not c[3]]  # Filter out candidates with errors
+        olmocr_candidates = sorted([c for c in valid_candidates if "olmocr" in c[0].lower()], key=lambda x: x[1], reverse=True)
+        non_olmocr_candidates = sorted([c for c in valid_candidates if "olmocr" not in c[0].lower()], key=lambda x: x[1], reverse=True)
+        
+        top_olmocr = olmocr_candidates[0] if olmocr_candidates else None
+        top_non_olmocr = non_olmocr_candidates[0] if non_olmocr_candidates else None
+        top_two_olmocr = olmocr_candidates[:2]
+
+        # Test 1: Top olmocr vs Top non-olmocr
+        if top_olmocr and top_non_olmocr:
+            olmocr_name, olmocr_score = top_olmocr[0], top_olmocr[1]
+            non_olmocr_name, non_olmocr_score = top_non_olmocr[0], top_non_olmocr[1]
+            olmocr_scores = top_olmocr[7]  # all_test_scores
+            non_olmocr_scores = top_non_olmocr[7]  # all_test_scores
+            
+            diff, p_value = perform_permutation_test(
+                olmocr_scores, non_olmocr_scores
+            )
+            
+            print(f"\nComparison 1: Top olmocr vs Top non-olmocr candidate")
+            print(f"  {olmocr_name} ({olmocr_score*100:.1f}%) vs {non_olmocr_name} ({non_olmocr_score*100:.1f}%)")
+            print(f"  Difference: {diff*100:.2f}% (positive means {olmocr_name} is better)")
+            print(f"  p-value: {p_value:.4f}")
+            if p_value < 0.05:
+                print(f"  Result: Statistically significant difference (p < 0.05)")
+            else:
+                print(f"  Result: No statistically significant difference (p ≥ 0.05)")
+        else:
+            print("\nCannot perform olmocr vs non-olmocr comparison: Missing candidates")
+        
+        # Test 2: Top two olmocr candidates (if there are at least two)
+        if len(top_two_olmocr) >= 2:
+            olmocr1_name, olmocr1_score = top_two_olmocr[0][0], top_two_olmocr[0][1]
+            olmocr2_name, olmocr2_score = top_two_olmocr[1][0], top_two_olmocr[1][1]
+            olmocr1_scores = top_two_olmocr[0][7]  # all_test_scores
+            olmocr2_scores = top_two_olmocr[1][7]  # all_test_scores
+            
+            diff, p_value = perform_permutation_test(
+                olmocr1_scores, olmocr2_scores
+            )
+            
+            print(f"\nComparison 2: Top two olmocr candidates")
+            print(f"  {olmocr1_name} ({olmocr1_score*100:.1f}%) vs {olmocr2_name} ({olmocr2_score*100:.1f}%)")
+            print(f"  Difference: {diff*100:.2f}% (positive means {olmocr1_name} is better)")
+            print(f"  p-value: {p_value:.4f}")
+            if p_value < 0.05:
+                print(f"  Result: Statistically significant difference (p < 0.05)")
+            else:
+                print(f"  Result: No statistically significant difference (p ≥ 0.05)")
+        else:
+            print("\nCannot perform top two olmocr comparison: Not enough olmocr candidates")
+        
+        print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()

olmocr/bench/convert.py

+import argparse
+import asyncio
+import glob
+import importlib
+import os
+from functools import partial
+from itertools import product
+
+from tqdm import tqdm
+from pypdf import PdfReader
+
+def parse_method_arg(method_arg):
+    """
+    Parse a method configuration string of the form:
+       method_name[:key=value[:key2=value2...]]
+    Returns:
+       (method_name, kwargs_dict, folder_name)
+    """
+    parts = method_arg.split(":")
+    name = parts[0]
+    kwargs = {}
+    folder_name = name  # Default folder name is the method name
+
+    for extra in parts[1:]:
+        if "=" in extra:
+            key, value = extra.split("=", 1)
+            if key == "name":
+                folder_name = value
+                continue
+
+            try:
+                converted = int(value)
+            except ValueError:
+                try:
+                    converted = float(value)
+                except ValueError:
+                    converted = value
+            kwargs[key] = converted
+        else:
+            raise ValueError(f"Extra argument '{extra}' is not in key=value format")
+
+    return name, kwargs, folder_name
+
+
+# Wrapper to run synchronous functions in the event loop
+async def run_sync_in_executor(func, *args, **kwargs):
+    """Run a synchronous function in the default executor"""
+    loop = asyncio.get_running_loop()
+    return await loop.run_in_executor(None, partial(func, *args, **kwargs))
+
+
+async def process_pdf(pdf_path, page_num, method, kwargs, output_path, is_async):
+    """Process a single PDF and save the result to output_path"""
+    try:
+        if is_async:
+            # Run async function directly
+            markdown = await method(pdf_path, page_num=page_num, **kwargs)
+        else:
+            # Run synchronous function in the executor
+            markdown = await run_sync_in_executor(method, pdf_path, page_num=page_num, **kwargs)
+
+        if markdown is None:
+            print(f"Warning, did not get output for {os.path.basename(output_path)}")
+            # Write blank to this file, so that it's marked as an error and not just skipped in evals
+            with open(output_path, "w") as out_f:
+                out_f.write("")
+            return False
+
+        # Write the markdown to the output file
+        with open(output_path, "w") as out_f:
+            out_f.write(markdown)
+            
+        return True
+    except Exception as ex:
+        print(f"Exception {str(ex)} occurred while processing {os.path.basename(output_path)}")
+        # Write blank to this file, so that it's marked as an error and not just skipped in evals
+        with open(output_path, "w") as out_f:
+            out_f.write("")
+        return False
+
+
+async def process_pdfs(config, pdf_directory, data_directory, repeats, force, max_parallel=None):
+    """
+    Process PDFs using asyncio for both sync and async methods, 
+    limiting the number of concurrent tasks to max_parallel.
+    """
+    for candidate in config.keys():
+        print(f"Starting conversion using {candidate} with kwargs: {config[candidate]['kwargs']}")
+        folder_name = config[candidate]["folder_name"]
+        candidate_output_dir = os.path.join(data_directory, folder_name)
+        os.makedirs(candidate_output_dir, exist_ok=True)
+
+        method = config[candidate]["method"]
+        kwargs = config[candidate]["kwargs"]
+        is_async = asyncio.iscoroutinefunction(method)
+
+        all_pdfs = glob.glob(os.path.join(pdf_directory, "*.pdf"))
+        all_pdfs.sort()
+
+        # Prepare all tasks
+        tasks = []
+        task_descriptions = {}
+        
+        for pdf_path in all_pdfs:
+            pdf = PdfReader(pdf_path)
+            num_pages = len(pdf.pages)
+
+            base_name = os.path.basename(pdf_path).replace(".pdf", "")
+            
+            for repeat in range(1, repeats + 1):
+                for page_num in range(1, num_pages + 1):
+                    output_filename = f"{base_name}_pg{page_num}_repeat{repeat}.md"
+                    output_path = os.path.join(candidate_output_dir, output_filename)
+                    
+                    if os.path.exists(output_path) and not force:
+                        print(f"Skipping {base_name}_pg{page_num}_repeat{repeat} for {candidate}, file already exists")
+                        print("Rerun with --force flag to force regeneration")
+                        continue
+                    
+                    task = process_pdf(pdf_path, page_num, method, kwargs, output_path, is_async)
+                    tasks.append(task)
+                    task_descriptions[id(task)] = f"{base_name}_pg{page_num}_repeat{repeat} ({candidate})"
+            
+        # Process tasks with semaphore to limit concurrency
+        semaphore = asyncio.Semaphore(max_parallel or 1)  # Default to 1 if not specified
+        
+        async def process_with_semaphore(task):
+            async with semaphore:
+                return await task
+        
+        # Wrap each task with the semaphore
+        limited_tasks = [process_with_semaphore(task) for task in tasks]
+        
+        # Process tasks with progress bar
+        if limited_tasks:
+            completed = 0
+            with tqdm(total=len(limited_tasks), desc=f"Processing {candidate}") as pbar:
+                for task in asyncio.as_completed(limited_tasks):
+                    try:
+                        result = await task
+                        if result:
+                            completed += 1
+                    except Exception as e:
+                        print(f"Task failed: {e}")
+                    finally:
+                        pbar.update(1)
+            
+            print(f"Completed {completed} out of {len(limited_tasks)} tasks for {candidate}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run PDF conversion using specified OCR methods and extra parameters.")
+    parser.add_argument(
+        "methods",
+        nargs="+",
+        help="Methods to run in the format method[:key=value ...]. "
+        "Example: gotocr mineru:temperature=2 marker:u=3. "
+        "Use 'name=folder_name' to specify a custom output folder name.",
+    )
+    parser.add_argument("--repeats", type=int, default=1, help="Number of times to repeat the conversion for each PDF.")
+    parser.add_argument("--dir", type=str, default=os.path.join(os.path.dirname(__file__), "sample_data"), help="Path to the data folder in which to save outputs, pdfs should be in /pdfs folder within it.")
+    parser.add_argument("--force", action="store_true", default=False, help="Force regenerating of output files, even if they already exist")
+    parser.add_argument("--parallel", type=int, default=1, help="Maximum number of concurrent tasks")
+    args = parser.parse_args()
+
+    # Mapping of method names to a tuple: (module path, function name)
+    available_methods = {
+        "olmocr_pipeline": ("olmocr.bench.runners.run_olmocr_pipeline", "run_olmocr_pipeline"),
+        "gotocr": ("olmocr.bench.runners.run_gotocr", "run_gotocr"),
+        "marker": ("olmocr.bench.runners.run_marker", "run_marker"),
+        "mineru": ("olmocr.bench.runners.run_mineru", "run_mineru"),
+        "chatgpt": ("olmocr.bench.runners.run_chatgpt", "run_chatgpt"),
+        "gemini": ("olmocr.bench.runners.run_gemini", "run_gemini"),
+        "mistral": ("olmocr.bench.runners.run_mistral", "run_mistral"),
+        "server": ("olmocr.bench.runners.run_server", "run_server"),
+    }
+
+    # Build config by importing only requested methods.
+    config = {}
+    for method_arg in args.methods:
+        method_name, extra_kwargs, folder_name = parse_method_arg(method_arg)
+        if method_name not in available_methods:
+            parser.error(f"Unknown method: {method_name}. " f"Available methods: {', '.join(available_methods.keys())}")
+        module_path, function_name = available_methods[method_name]
+        # Dynamically import the module and get the function.
+        module = importlib.import_module(module_path)
+        function = getattr(module, function_name)
+        config[method_name] = {"method": function, "kwargs": extra_kwargs, "folder_name": folder_name}
+
+    data_directory = args.dir
+    pdf_directory = os.path.join(data_directory, "pdfs")
+
+    # Run the async process function with the parallel argument
+    asyncio.run(process_pdfs(config, pdf_directory, data_directory, args.repeats, args.force, args.parallel))
\ No newline at end of file

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olmocr/bench/katex/render.py

+#!/usr/bin/env python3
+"""
+Extract inner-most spans and their bounding boxes, and the mathML output, 
+from rendered LaTeX equations using Playwright and KaTeX.
+Caching is maintained via a SHA1-based hash stored as a JSON file.
+
+Requirements:
+    pip install playwright
+    python -m playwright install chromium
+
+    Place katex.min.css and katex.min.js in the same directory as this script
+"""
+
+import os
+import re
+import html
+import hashlib
+import pathlib
+import json
+import re
+import shutil
+from dataclasses import dataclass
+from typing import List
+import unittest
+import html.entities
+from lxml import etree
+
+from playwright.sync_api import sync_playwright, Error as PlaywrightError
+
+@dataclass
+class BoundingBox:
+    x: float
+    y: float
+    width: float
+    height: float
+
+@dataclass
+class SpanInfo:
+    text: str
+    bounding_box: BoundingBox
+
+@dataclass
+class RenderedEquation:
+    mathml: str
+    spans: List[SpanInfo]
+
+def get_equation_hash(equation, bg_color="white", text_color="black", font_size=24):
+    """
+    Calculate SHA1 hash of the equation string and rendering parameters.
+    """
+    params_str = f"{equation}|{bg_color}|{text_color}|{font_size}"
+    return hashlib.sha1(params_str.encode('utf-8')).hexdigest()
+
+def get_cache_dir():
+    """
+    Get the cache directory for equations, creating it if it doesn't exist.
+    """
+    cache_dir = pathlib.Path.home() / '.cache' / 'olmocr' / 'bench' / 'equations'
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    return cache_dir
+
+
+def clear_cache_dir():
+    """
+    Clear all files and subdirectories in the cache directory.
+    """
+    cache_dir = get_cache_dir()
+    if cache_dir.exists() and cache_dir.is_dir():
+        shutil.rmtree(cache_dir)
+        cache_dir.mkdir(parents=True, exist_ok=True)  # Recreate the empty directory
+
+
+def render_equation(
+    equation, 
+    bg_color="white",
+    text_color="black",
+    font_size=24,
+    use_cache=True,
+    debug_dom=False,
+):
+    """
+    Render a LaTeX equation using Playwright and KaTeX, extract the inner-most span elements
+    (those without child elements that contain non-whitespace text) along with their bounding boxes,
+    and also extract the MathML output generated by KaTeX.
+
+    Returns:
+        RenderedEquation: A dataclass containing the mathml string and a list of SpanInfo dataclasses.
+    """
+    # Calculate hash for caching
+    eq_hash = get_equation_hash(equation, bg_color, text_color, font_size)
+    cache_dir = get_cache_dir()
+    cache_file = cache_dir / f"{eq_hash}.json"
+    cache_error_file = cache_dir / f"{eq_hash}_error"
+    
+    if use_cache:
+        if cache_error_file.exists():
+            return None
+        if cache_file.exists():
+            with open(cache_file, 'r') as f:
+                data = json.load(f)
+            spans = [
+                SpanInfo(
+                    text=s["text"],
+                    bounding_box=BoundingBox(
+                        x=s["boundingBox"]["x"],
+                        y=s["boundingBox"]["y"],
+                        width=s["boundingBox"]["width"],
+                        height=s["boundingBox"]["height"],
+                    )
+                )
+                for s in data["spans"]
+            ]
+            return RenderedEquation(mathml=data["mathml"], spans=spans)
+
+    # Escape backslashes for JavaScript string
+    escaped_equation = json.dumps(equation)
+    
+    # Get local paths for KaTeX files
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    katex_css_path = os.path.join(script_dir, "katex.min.css")
+    katex_js_path = os.path.join(script_dir, "katex.min.js")
+    
+    if not os.path.exists(katex_css_path) or not os.path.exists(katex_js_path):
+        raise FileNotFoundError(f"KaTeX files not found. Please ensure katex.min.css and katex.min.js are in {script_dir}")
+    
+    with sync_playwright() as p:
+        browser = p.chromium.launch()
+        page = browser.new_page(viewport={"width": 800, "height": 400})
+        
+        # Basic HTML structure
+        page_html = f"""
+        <!DOCTYPE html>
+        <html>
+        <head>
+            <style>
+                body {{
+                    display: flex;
+                    justify-content: center;
+                    align-items: center;
+                    height: 100vh;
+                    margin: 0;
+                    background-color: {bg_color};
+                    color: {text_color};
+                }}
+                #equation-container {{
+                    padding: 0;
+                    font-size: {font_size}px;
+                }}
+            </style>
+        </head>
+        <body>
+            <div id="equation-container"></div>
+        </body>
+        </html>
+        """
+        page.set_content(page_html)
+        page.add_style_tag(path=katex_css_path)
+        page.add_script_tag(path=katex_js_path)
+        page.wait_for_load_state("networkidle")
+        
+        katex_loaded = page.evaluate("typeof katex !== 'undefined'")
+        if not katex_loaded:
+            raise RuntimeError("KaTeX library failed to load. Check your katex.min.js file.")
+        
+        try:
+            error_message = page.evaluate(f"""
+            () => {{
+                try {{
+                    katex.render({escaped_equation}, document.getElementById("equation-container"), {{
+                        displayMode: true,
+                        throwOnError: true
+                    }});
+                    return null;
+                }} catch (error) {{
+                    console.error("KaTeX error:", error.message);
+                    return error.message;
+                }}
+            }}
+            """)
+        except PlaywrightError as ex:
+            print(escaped_equation)
+            error_message = str(ex)
+            raise 
+
+        if error_message:
+            print(f"Error rendering equation: '{equation}'")
+            print(error_message)
+            cache_error_file.touch()
+            browser.close()
+            return None
+        
+        page.wait_for_selector(".katex", state="attached")
+        
+        if debug_dom:
+            katex_dom_html = page.evaluate("""
+            () => {
+                return document.getElementById("equation-container").innerHTML;
+            }
+            """)
+            print("\n===== KaTeX DOM HTML =====")
+            print(katex_dom_html)
+        
+        # Extract inner-most spans with non-whitespace text
+        spans_info = page.evaluate("""
+        () => {
+            const spans = Array.from(document.querySelectorAll('span'));
+            const list = [];
+            spans.forEach(span => {
+                // Check if this span has no child elements and contains non-whitespace text
+                if (span.children.length === 0 && /\S/.test(span.textContent)) {
+                    const rect = span.getBoundingClientRect();
+                    list.push({
+                        text: span.textContent.trim(),
+                        boundingBox: {
+                            x: rect.x,
+                            y: rect.y,
+                            width: rect.width,
+                            height: rect.height
+                        }
+                    });
+                }
+            });
+            return list;
+        }
+        """)
+        
+        if debug_dom:
+            print("\n===== Extracted Span Information =====")
+            print(spans_info)
+        
+        # Extract mathML output (if available) from the KaTeX output.
+        # We try to get the <math> element within an element with class "katex-mathml".
+        mathml = page.evaluate("""
+        () => {
+            const mathElem = document.querySelector('.katex-mathml math');
+            return mathElem ? mathElem.outerHTML : "";
+        }
+        """)
+        
+        browser.close()
+        
+        # Build the result as a RenderedEquation dataclass
+        rendered_eq = RenderedEquation(
+            mathml=mathml,
+            spans=[
+                SpanInfo(
+                    text=s["text"],
+                    bounding_box=BoundingBox(
+                        x=s["boundingBox"]["x"],
+                        y=s["boundingBox"]["y"],
+                        width=s["boundingBox"]["width"],
+                        height=s["boundingBox"]["height"]
+                    )
+                )
+                for s in spans_info
+            ]
+        )
+        
+        # Save to cache (convert dataclasses to a JSON-serializable dict)
+        cache_data = {
+            "mathml": rendered_eq.mathml,
+            "spans": [
+                {
+                    "text": span.text,
+                    "boundingBox": {
+                        "x": span.bounding_box.x,
+                        "y": span.bounding_box.y,
+                        "width": span.bounding_box.width,
+                        "height": span.bounding_box.height
+                    }
+                }
+                for span in rendered_eq.spans
+            ]
+        }
+        with open(cache_file, 'w') as f:
+            json.dump(cache_data, f)
+        return rendered_eq
+
+def compare_rendered_equations(reference: RenderedEquation, hypothesis: RenderedEquation) -> bool:
+    """
+    First, try to determine whether the normalized MathML of the hypothesis is contained
+    as a substring of the normalized MathML of the reference.
+    
+    If that fails, then perform a neighbor‐based matching:
+    Each span in the hypothesis must be matched to a span in the reference (with identical text),
+    and if a hypothesis span has an immediate neighbor (up, down, left, or right), then its candidate
+    reference span must have a neighbor in the same direction that (if already matched) is the candidate
+    for the hypothesis neighbor – otherwise, the candidate must have the same text as the hypothesis neighbor.
+    The algorithm uses backtracking to explore all possible assignments.
+    """
+    from bs4 import BeautifulSoup
+
+    def extract_inner(mathml: str) -> str:
+        try:
+            # Use the "xml" parser so that BeautifulSoup parses MathML correctly,
+            # handling HTML entities along the way.
+            soup = BeautifulSoup(mathml, "xml")
+            semantics = soup.find("semantics")
+            if semantics:
+                # Concatenate the string representation of all children except <annotation>
+                inner_parts = [
+                    str(child)
+                    for child in semantics.contents
+                    if getattr(child, "name", None) != "annotation"
+                ]
+                return ''.join(inner_parts)
+            else:
+                return str(soup)
+        except Exception as e:
+            print("Error parsing MathML with BeautifulSoup:", e)
+            print(mathml)
+            return mathml
+
+    def normalize(s: str) -> str:
+        return re.sub(r'\s+', '', s)
+
+    # First, try a fast mathML substring check.
+    reference_inner = normalize(extract_inner(reference.mathml))
+    hypothesis_inner = normalize(extract_inner(hypothesis.mathml))
+    if reference_inner in hypothesis_inner:
+        return True
+
+    # Fallback: neighbor-based matching using the spans.
+    # First, print out the original span lists.
+    # print("Hypothesis spans:")
+    # for s in hypothesis.spans:
+    #     print(s)
+    # print("---")
+    # print("Reference spans:")
+    # for s in reference.spans:
+    #     print(s)
+    # print("---")
+
+    # We swap H and R so that we are effectively checking if the reference is contained in the hypothesis.
+    H, R = reference.spans, hypothesis.spans
+
+    H = [span for span in H if span.text != "\u200b"]
+    R = [span for span in R if span.text != "\u200b"]
+
+    # Build candidate map: for each span in H (reference), record indices in R with matching text.
+    candidate_map = {}
+    for i, hspan in enumerate(H):
+        candidate_map[i] = [j for j, rsp in enumerate(R) if rsp.text == hspan.text]
+        if not candidate_map[i]:
+            return False  # no candidate for a given span, so we fail immediately
+   
+    # print("Candidate Map:")
+    # print(candidate_map)
+
+    # Function to compute neighbor mappings for a list of spans.
+    def compute_neighbors(spans, tol=5):
+        neighbors = {}
+        for i, span in enumerate(spans):
+            cx = span.bounding_box.x + span.bounding_box.width / 2
+            cy = span.bounding_box.y + span.bounding_box.height / 2
+            up = down = left = right = None
+            up_dist = down_dist = left_dist = right_dist = None
+            for j, other in enumerate(spans):
+                if i == j:
+                    continue
+                ocx = other.bounding_box.x + other.bounding_box.width / 2
+                ocy = other.bounding_box.y + other.bounding_box.height / 2
+                # Up: candidate must be above (ocy < cy) and nearly aligned horizontally.
+                if ocy < cy and abs(ocx - cx) <= tol:
+                    dist = cy - ocy
+                    if up is None or dist < up_dist:
+                        up = j
+                        up_dist = dist
+                # Down: candidate below.
+                if ocy > cy and abs(ocx - cx) <= tol:
+                    dist = ocy - cy
+                    if down is None or dist < down_dist:
+                        down = j
+                        down_dist = dist
+                # Left: candidate left.
+                if ocx < cx and abs(ocy - cy) <= tol:
+                    dist = cx - ocx
+                    if left is None or dist < left_dist:
+                        left = j
+                        left_dist = dist
+                # Right: candidate right.
+                if ocx > cx and abs(ocy - cy) <= tol:
+                    dist = ocx - cx
+                    if right is None or dist < right_dist:
+                        right = j
+                        right_dist = dist
+            neighbors[i] = {"up": up, "down": down, "left": left, "right": right}
+        return neighbors
+
+    hyp_neighbors = compute_neighbors(H)
+    ref_neighbors = compute_neighbors(R)
+    # print("Neighbor Map for Reference spans (H):")
+    # for i, nb in hyp_neighbors.items():
+    #     print(f"Span {i}: {nb}")
+    # print("Neighbor Map for Hypothesis spans (R):")
+    # for i, nb in ref_neighbors.items():
+    #     print(f"Span {i}: {nb}")
+
+    # Backtracking search for an injection f: {0,...,n-1} -> {indices in R} that preserves neighbor relations.
+    n = len(H)
+    used = [False] * len(R)
+    assignment = {}
+
+    def backtrack(i):
+        if i == n:
+            return True
+        for cand in candidate_map[i]:
+            if used[cand]:
+                continue
+            # Tentatively assign hypothesis span i (from H) to reference span cand (in R).
+            assignment[i] = cand
+            used[cand] = True
+            valid = True
+            # Check neighbor constraints for all directions.
+            for direction in ["up", "down", "left", "right"]:
+                hyp_nb = hyp_neighbors[i].get(direction)
+                ref_nb = ref_neighbors[cand].get(direction)
+                if hyp_nb is not None:
+                    expected_text = H[hyp_nb].text
+                    # The candidate in R must have a neighbor in that direction.
+                    if ref_nb is None:
+                        valid = False
+                        break
+                    # If the neighbor in H is already assigned, then the candidate neighbor must match.
+                    if hyp_nb in assignment:
+                        if assignment[hyp_nb] != ref_nb:
+                            valid = False
+                            break
+                    else:
+                        # If not yet assigned, the neighbor text in R must match the expected text.
+                        if R[ref_nb].text != expected_text:
+                            valid = False
+                            break
+            if valid:
+                if backtrack(i + 1):
+                    return True
+            # Backtrack this candidate assignment.
+            used[cand] = False
+            del assignment[i]
+        return False
+
+    result = backtrack(0)
+
+    return result
+
+
+
+class TestRenderedEquationComparison(unittest.TestCase):
+    def test_exact_match(self):
+        # Both calls with identical LaTeX should produce matching MathML output.
+        eq1 = render_equation("a+b", use_cache=False)
+        eq2 = render_equation("a+b", use_cache=False)
+        self.assertTrue(compare_rendered_equations(eq1, eq2))
+    
+    def test_whitespace_difference(self):
+        # Differences in whitespace in the LaTeX input should not affect the MathML output.
+        eq1 = render_equation("a+b", use_cache=False)
+        eq2 = render_equation("a + b", use_cache=False)
+        self.assertTrue(compare_rendered_equations(eq1, eq2))
+    
+    def test_not_found(self):
+        # Completely different equations should not match.
+        eq1 = render_equation("c-d", use_cache=False)
+        eq2 = render_equation("a+b", use_cache=False)
+        self.assertFalse(compare_rendered_equations(eq1, eq2))
+    
+    def test_align_block_contains_needle(self):
+        # The MathML output of the plain equation should be found within the align block output.
+        eq_plain = render_equation("a+b", use_cache=False)
+        eq_align = render_equation("\\begin{align*}a+b\\end{align*}", use_cache=False)
+        self.assertTrue(compare_rendered_equations(eq_plain, eq_align))
+    
+    def test_align_block_needle_not_in(self):
+        # An align block rendering a different equation should not contain the MathML of an unrelated equation.
+        eq_align = render_equation("\\begin{align*}a+b\\end{align*}", use_cache=False)
+        eq_diff = render_equation("c-d", use_cache=False)
+        self.assertFalse(compare_rendered_equations(eq_diff, eq_align))
+
+    def test_big(self):
+        ref_rendered = render_equation("\\nabla \\cdot \\mathbf{E} = \\frac{\\rho}{\\varepsilon_0}", use_cache=False, debug_dom=False)
+        align_rendered = render_equation("""\\begin{align*}\\nabla \\cdot \\mathbf{E} = \\frac{\\rho}{\\varepsilon_0}\\end{align*}""", use_cache=False, debug_dom=False)
+        self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered))
+
+    def test_dot_end1(self):
+        ref_rendered = render_equation("\\lambda_{g}=\\sum_{s \\in S} \\zeta_{n}^{\\psi(g s)}=\\sum_{i=1}^{k}\\left[\\sum_{s, R s=\\mathcal{I}_{i}} \\zeta_{n}^{\\varphi(g s)}\\right]")
+        align_rendered = render_equation("\\lambda_{g}=\\sum_{s \\in S} \\zeta_{n}^{\\psi(g s)}=\\sum_{i=1}^{k}\\left[\\sum_{s, R s=\\mathcal{I}_{i}} \\zeta_{n}^{\\varphi(g s)}\\right].")
+        self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered))
+
+    def test_dot_end2(self):
+        ref_rendered = render_equation("\\lambda_{g}=\\sum_{s \\in S} \\zeta_{n}^{\\psi(g s)}=\\sum_{i=1}^{k}\\left[\\sum_{s, R s=\\mathcal{I}_{i}} \\zeta_{n}^{\\psi(g s)}\\right]")
+        align_rendered = render_equation("\\lambda_g = \\sum_{s \\in S} \\zeta_n^{\\psi(gs)} = \\sum_{i=1}^{k} \\left[ \\sum_{s, Rs = \\mathcal{I}_i} \\zeta_n^{\\psi(gs)} \\right]")
+        self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered))
+
+    def test_lambda(self):
+        ref_rendered = render_equation("\\lambda_g = \\lambda_{g'}")
+        align_rendered = render_equation("\\lambda_{g}=\\lambda_{g^{\\prime}}")
+        self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered))
+
+    def test_gemini(self):
+        ref_rendered = render_equation("u \\in (R/\\operatorname{Ann}_R(x_i))^{\\times}")
+        align_rendered = render_equation("u \\in\\left(R / \\operatorname{Ann}_{R}\\left(x_{i}\\right)\\right)^{\\times}")
+        self.assertTrue(compare_rendered_equations(ref_rendered, align_rendered))
+        
+if __name__ == "__main__":
+    unittest.main()

olmocr/bench/miners/download_math.py

+# This script goes to
+# https://arxiv.org/list/math/recent?skip=0&show=2000
+# and downloads all the source PDFs, as well as latex equivalents, and puts them together into 
+# Searching for:
+# <a href="/pdf/2503.08675" title="Download PDF" id="pdf-2503.08675" aria-labelledby="pdf-2503.08675">pdf</a>
+# a math_data folder
+#!/usr/bin/env python3
+import argparse
+import os
+import re
+import time
+import io
+import tarfile
+import requests
+from tqdm import tqdm
+
+def download_and_extract_source(paper_id, data_dir):
+    source_url = f"https://export.arxiv.org/src/{paper_id}"
+    print(f"Downloading source for {paper_id} from {source_url}...")
+    response = requests.get(source_url)
+    if response.status_code != 200:
+        print(f"Error downloading source for {paper_id}: HTTP {response.status_code}")
+        return False
+
+    # Try to open as a tar archive.
+    try:
+        file_obj = io.BytesIO(response.content)
+        with tarfile.open(fileobj=file_obj, mode='r:*') as tar:
+            # Filter for regular .tex files.
+            members = [m for m in tar.getmembers() if m.isfile() and m.name.endswith('.tex')]
+            print("Found TeX files:", [m.name for m in members])
+            if len(members) == 1:
+                member = members[0]
+                extracted = tar.extractfile(member)
+                if extracted is None:
+                    print(f"Error extracting {paper_id}: Could not read the file from the archive.")
+                    return False
+                content = extracted.read()
+                out_path = os.path.join(data_dir, f"{paper_id}.tex")
+                with open(out_path, "wb") as f:
+                    f.write(content)
+                print(f"Saved tex source for {paper_id} as {out_path}")
+                return True
+            else:
+                print(f"Error: {paper_id} contains multiple .tex files or none. Skipping extraction.")
+                return False
+    except tarfile.ReadError:
+        # Not a tar archive; assume it's a single file.
+        out_path = os.path.join(data_dir, f"{paper_id}.tex")
+        with open(out_path, "wb") as f:
+            f.write(response.content)
+        print(f"Saved non-archive tex source for {paper_id} as {out_path}")
+        return True
+
+def download_pdf(paper_id, data_dir):
+    pdf_url = f"https://arxiv.org/pdf/{paper_id}.pdf"
+    print(f"Downloading PDF for {paper_id} from {pdf_url}...")
+    response = requests.get(pdf_url)
+    if response.status_code != 200:
+        print(f"Error downloading PDF for {paper_id}: HTTP {response.status_code}")
+        return False
+    out_path = os.path.join(data_dir, f"{paper_id}.pdf")
+    with open(out_path, "wb") as f:
+        f.write(response.content)
+    print(f"Saved PDF for {paper_id} as {out_path}")
+    return True
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Download and extract arXiv LaTeX source files and PDFs only if both succeed."
+    )
+    parser.add_argument(
+        "--url",
+        type=str,
+        default="https://arxiv.org/list/math/recent?skip=0&show=2000",
+        help="URL of the arXiv list page to scrape (default: %(default)s)"
+    )
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default="math_data/pdfs",
+        help="Directory to save downloaded files (default: %(default)s)"
+    )
+    args = parser.parse_args()
+
+    if not os.path.exists(args.data_dir):
+        os.makedirs(args.data_dir)
+
+    print(f"Downloading list page from {args.url}...")
+    response = requests.get(args.url)
+    if response.status_code != 200:
+        print(f"Error downloading list page: HTTP {response.status_code}")
+        return
+
+    # Find all pdf links in the form: <a href="/pdf/2503.08675" ...>pdf</a>
+    pattern = re.compile(r'href="/pdf/(\d+\.\d+)"')
+    paper_ids = pattern.findall(response.text)
+    print(f"Found {len(paper_ids)} papers.")
+
+    # For each paper, only keep the files if both the tex extraction and pdf download succeed.
+    for paper_id in tqdm(paper_ids):
+        tex_success = download_and_extract_source(paper_id, args.data_dir)
+        if not tex_success:
+            print(f"Skipping PDF download for {paper_id} because tex extraction failed.")
+            continue
+        
+        pdf_success = download_pdf(paper_id, args.data_dir)
+        if not pdf_success:
+            # Remove the tex file if the PDF download fails.
+            tex_path = os.path.join(args.data_dir, f"{paper_id}.tex")
+            if os.path.exists(tex_path):
+                os.remove(tex_path)
+                print(f"Removed tex file for {paper_id} because PDF download failed.")
+        time.sleep(1)
+
+if __name__ == "__main__":
+    main()

olmocr/bench/miners/mine_diffs.py

+import argparse
+import base64
+import os
+import re
+import time
+from collections import Counter
+from difflib import SequenceMatcher
+
+import syntok.segmenter as segmenter
+from google import genai
+from google.genai import types
+
+from olmocr.bench.tests import TextPresenceTest, save_tests
+from olmocr.data.renderpdf import render_pdf_to_base64png
+
+LABEL_WIDTH = 8  # fixed width for printing labels
+
+# Uses a gemini prompt to get the most likely clean sentence from a pdf page
+last_gemini_call = time.perf_counter()
+
+
+def clean_base_sentence(pdf_path: str, page_num: int, base_sentence: str) -> str:
+    client = genai.Client(
+        api_key=os.environ.get("GEMINI_API_KEY"),
+    )
+
+    image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=2048)
+    image_part = types.Part(inline_data=types.Blob(mime_type="image/png", data=base64.b64decode(image_base64)))
+    model = "gemini-2.0-flash-thinking-exp-01-21"  # Consider using a more stable model for production
+    # model="gemini-2.0-flash-001"
+    contents = [
+        types.Content(
+            role="user",
+            parts=[
+                image_part,
+                types.Part.from_text(
+                    text=f"""Base: {base_sentence}
+
+Consider the sentence labeled "Base" above in the document image attached. What is the correct reading of this document within the image of the page? I need it to be exact down to the individual character and that's very important to get right. It needs to match the picture, not the provided text. Please just output the correct full sentence exactly how it appears in the document image and nothing else. You can merge hyphenated words back together, and don't output any new lines."""
+                ),
+            ],
+        ),
+    ]
+    generate_content_config = types.GenerateContentConfig(
+        temperature=0.7,
+        top_p=0.95,
+        top_k=64,
+        max_output_tokens=500,
+        response_mime_type="text/plain",
+    )
+
+    response = client.models.generate_content(
+        model=model,
+        contents=contents,
+        config=generate_content_config,
+    )
+
+    # Basic rate limitting
+    global last_gemini_call
+    if time.perf_counter() - last_gemini_call < 6:
+        time.sleep(6 - (time.perf_counter() - last_gemini_call))
+
+    last_gemini_call = time.perf_counter()
+
+    # Return response
+    if response is not None and response.candidates is not None and len(response.candidates) > 0:
+        return response.candidates[0].content.parts[0].text
+    else:
+        return None
+
+
+def parse_sentences(text: str) -> list[str]:
+    """
+    Splits a text into a list of sentence strings using syntok.
+    Preserves original spacing and punctuation.
+    """
+    sentences = []
+    for paragraph in segmenter.process(text):
+        for sentence in paragraph:
+            # Reconstruct the sentence with original spacing
+            sentence_str = ""
+            for token in sentence:
+                sentence_str += token.spacing + token.value
+            # Trim any leading whitespace
+            sentence_str = sentence_str.lstrip()
+            sentences.append(sentence_str)
+    return sentences
+
+
+def compare_votes_for_file(base_pdf_file: str, base_pdf_page: int, base_text: str, candidate_texts: list[str], max_diffs: int) -> None:
+    """
+    For each sentence in the base text, finds the best matching sentence from
+    each candidate text (using a similarity threshold). If any candidate sentences
+    differ from the base sentence, collects that diff (base sentence plus variant
+    votes) for later printing. At the end, prints only the top N diffs (by total vote count)
+    for the file.
+
+    Comparison is case-insensitive, but output preserves original capitalization.
+    """
+    base_sentences = parse_sentences(base_text)
+    # Parse all candidate texts into lists of sentences
+    candidate_sentences_list = [parse_sentences(ct) for ct in candidate_texts]
+
+    diffs = []  # list to hold diff entries
+    for b_sentence in base_sentences:
+        b_sentence = b_sentence.replace("\n", " ").strip()
+
+        votes = []
+        for c_sentences in candidate_sentences_list:
+            best_ratio = 0.0
+            best_candidate = None
+
+            # Find the candidate sentence with the highest similarity to b_sentence
+            # using case-insensitive comparison
+            for c_sentence in c_sentences:
+                ratio = SequenceMatcher(None, b_sentence.lower(), c_sentence.lower()).ratio()
+                if ratio > best_ratio:
+                    best_ratio = ratio
+                    best_candidate = c_sentence  # Keep original capitalization for output
+
+            # Append the candidate if it passes the similarity threshold (e.g., 0.7)
+            if best_ratio > 0.5 and best_candidate is not None:
+                votes.append(best_candidate.strip())
+
+        # Only consider variants that differ when compared case-insensitively
+        variant_votes = [vote for vote in votes if vote.lower() != b_sentence.lower()]
+        if variant_votes:
+            diff_entry = {
+                "base": b_sentence,
+                "variants": Counter(variant_votes),
+                "vote_count": len(variant_votes),
+            }
+            diffs.append(diff_entry)
+
+    # Sort diffs by vote_count descending and take only the top max_diffs
+    diffs.sort(key=lambda d: d["vote_count"], reverse=True)
+    top_diffs = diffs[:max_diffs]
+    tests = []
+
+    for index, diff in enumerate(top_diffs):
+        base_sentence = diff["base"]
+        variant_counter = diff["variants"]
+
+        # Print base sentence using fixed-width label formatting
+        print(f"{'Base:':<{LABEL_WIDTH}} {base_sentence}")
+        print(f"{'Variants:':<{LABEL_WIDTH}}")
+        for variant, count in variant_counter.items():
+            label = f"{count}x:"
+            print(f"{label:<{LABEL_WIDTH}} {variant}")
+        # Get the clean version of the sentence
+        cleaned = clean_base_sentence(base_pdf_file, base_pdf_page, base_sentence)
+        print(f"{'Clean:':<{LABEL_WIDTH}} {cleaned}")
+        print("-" * 40)
+
+        if cleaned is None:
+            cleaned = base_sentence
+
+        tests.append(
+            TextPresenceTest(
+                pdf=os.path.basename(base_pdf_file),
+                page=base_pdf_page,
+                id=f"{os.path.basename(base_pdf_file).replace('.pdf', '')}_minediff_{index:02d}",
+                type="present",
+                threshold=1.0,
+                text=cleaned,
+            )
+        )
+
+    return tests
+
+
+def get_pdf_from_md(md_path: str) -> str:
+    base = os.path.basename(md_path)
+    base = re.sub(r"_\d+\.md$", ".pdf", base)
+    return os.path.join(os.path.dirname(md_path), "..", "pdfs", base)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Compares sentences from base and candidate texts, printing differences.")
+    parser.add_argument("--base", default=os.path.join(os.path.dirname(__file__), "chatgpt"), help="Path to the folder containing base .md files.")
+    parser.add_argument("--compare", default=os.path.join(os.path.dirname(__file__), "olmocr"), help="Path to the folder containing candidate .md files.")
+    parser.add_argument("--max-diffs", type=int, default=5, help="Maximum number of diffs to display per file.")
+    parser.add_argument(
+        "--output", default="mine_diffs_candidates.jsonl", type=str, help="Output of potential candidate test proposals, to be verified or added to dataset"
+    )
+    args = parser.parse_args()
+
+    base_path = args.base
+    compare_path = args.compare
+    max_diffs = args.max_diffs
+
+    # Collect all .md files from the base and compare folders
+    base_files = [f for f in os.listdir(base_path) if f.endswith(".md")]
+
+    all_tests = []
+
+    # Process each base file and print out the vote differences
+    for bf in base_files:
+        base_file_path = os.path.join(base_path, bf)
+        with open(base_file_path, "r", encoding="utf-8") as f:
+            base_text = f.read()
+
+        compare_files = [f for f in os.listdir(compare_path) if f.endswith(".md") and re.sub(r"_\d+\.md$", "", f) == re.sub(r"_\d+\.md$", "", bf)]
+        
+        if not compare_files:
+            print(f"skipping {bf} nothing to compare against")
+
+        # Read all candidate texts at once
+        candidate_texts = []
+        for cf in compare_files:
+            with open(os.path.join(compare_path, cf), "r", encoding="utf-8") as f:
+                candidate_texts.append(f.read())
+
+        base_pdf_file = get_pdf_from_md(base_file_path)
+        base_pdf_page = 1
+        print(f"Results for base file: {bf}")
+        tests = compare_votes_for_file(base_pdf_file, base_pdf_page, base_text, candidate_texts, max_diffs)
+        all_tests.extend(tests)
+        print("")
+
+        # Output test candidates for review after each file, in case there are errors
+        save_tests(all_tests, args.output)
+
+
+if __name__ == "__main__":
+    main()

olmocr/bench/miners/mine_math.py

+#!/usr/bin/env python3
+"""
+mine_math.py - Extract and validate math equations from candidate files and TeX bases.
+
+This upgraded version:
+  • Uses the Python logging module for cleaner logging.
+  • Uses tqdm to display a progress bar.
+  • Uses ProcessPoolExecutor to process TeX file groups in parallel.
+  • For each TeX file, shuffles its pages randomly and processes them one-by-one.
+    Once three pages return at least one equation each, further pages are skipped.
+  • Adds an argparse argument for the similarity threshold for matches.
+  • Saves JSONL outputs incrementally as each TeX file group is processed.
+
+Usage:
+  python mine_math.py --math_data /path/to/math_data --candidate candidate_folder --output_file math_tests.jsonl
+    [--max_pages 3] [--parallel 8] [--sim_threshold 0.7]
+"""
+
+import argparse
+import glob
+import os
+import re
+import random
+import json
+import logging
+from typing import List, Optional, Tuple, Dict
+
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+from fuzzysearch import find_near_matches
+from rapidfuzz import fuzz
+from tqdm import tqdm
+
+from olmocr.bench.tests import MathTest  # Assumes MathTest is JSON serializable or has __dict__
+from olmocr.bench.tests import save_tests  # Original saving function (not used for incremental save)
+from olmocr.bench.katex.render import render_equation
+
+import numpy as np
+import numba
+
+
+# --- Logging Setup ---
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+# --- Utility Functions ---
+
+def normalize_text(text: str) -> str:
+    """Normalize text for better matching."""
+    text = re.sub(r'\s+', " ", text)
+    replacements = {
+        "'": "'",
+        "‚": "'",
+        '"': '"',
+        "„": '"',
+        "＿": "_",
+        "–": "-", "—": "-", "‑": "-", "‒": "-"
+    }
+    for fancy_char, ascii_char in replacements.items():
+        text = text.replace(fancy_char, ascii_char)
+    return text
+
+
+def extract_tex_content(tex_file: str) -> str:
+    """Extract the content from a TeX file."""
+    try:
+        with open(tex_file, 'r', encoding='utf-8') as f:
+            return f.read()
+    except UnicodeDecodeError:
+        try:
+            with open(tex_file, 'r', encoding='latin-1') as f:
+                return f.read()
+        except Exception as e:
+            logging.error("Error reading %s: %s", tex_file, e)
+            return ""
+
+
+def extract_candidate_content(candidate_file: str) -> str:
+    """Extract the content from a candidate .md file."""
+    try:
+        with open(candidate_file, 'r', encoding='utf-8') as f:
+            return f.read()
+    except Exception as e:
+        logging.error("Error reading %s: %s", candidate_file, e)
+        return ""
+
+
+def extract_math_from_tex(tex_content: str) -> List[Tuple[str, str]]:
+    """
+    Extract math equations from TeX content.
+    Returns list of tuples (equation_type, equation_content)
+    """
+    math_equations = []
+
+    # Patterns for display math
+    display_patterns = [
+        (r'\$\$(.*?)\$\$', '$$'),
+        (r'\\begin\{equation\}(.*?)\\end\{equation\}', 'equation'),
+        (r'\\begin\{equation\*\}(.*?)\\end\{equation\*\}', 'equation*'),
+        (r'\\begin\{align\}(.*?)\\end\{align\}', 'align'),
+        (r'\\begin\{align\*\}(.*?)\\end\{align\*\}', 'align*'),
+        (r'\\begin\{displaymath\}(.*?)\\end\{displaymath\}', 'displaymath'),
+        (r'\\\[(.*?)\\\]', 'displaymath')
+    ]
+    # Patterns for inline math
+    inline_patterns = [
+        (r'\$(.*?)\$', 'inline'),
+        (r'\\\((.*?)\\\)', 'inline')
+    ]
+
+    for pattern_list in [display_patterns, inline_patterns]:
+        for pattern, eq_type in pattern_list:
+            matches = re.finditer(pattern, tex_content, re.DOTALL)
+            for match in matches:
+                equation = match.group(1).strip()
+                if equation and not equation.isspace():
+                    math_equations.append((eq_type, equation))
+    return math_equations
+
+
+@numba.njit
+def compute_dp(candidate_arr, text_arr):
+    m = candidate_arr.shape[0]
+    n = text_arr.shape[0]
+    dp = np.empty((m + 1, n + 1), dtype=np.int32)
+    # For empty candidate, cost is 0 (can match anywhere in text)
+    for j in range(n + 1):
+        dp[0, j] = 0
+    # When text is empty, need to delete all candidate characters.
+    for i in range(1, m + 1):
+        dp[i, 0] = i
+
+    for i in range(1, m + 1):
+        for j in range(1, n + 1):
+            cost = 0 if candidate_arr[i - 1] == text_arr[j - 1] else 1
+            dp[i, j] = min(dp[i - 1, j - 1] + cost,  # substitution or match
+                           dp[i - 1, j] + 1,         # deletion (from candidate)
+                           dp[i, j - 1] + 1)         # insertion (in candidate)
+    return dp
+
+
+@numba.njit
+def find_best_end(dp, m, n):
+    best_distance = 1 << 30  # a large number
+    best_end = 0
+    for j in range(n + 1):
+        if dp[m, j] < best_distance:
+            best_distance = dp[m, j]
+            best_end = j
+    return best_end, best_distance
+
+
+@numba.njit
+def backtrack(dp, candidate_arr, text_arr, m, best_end):
+    i = m
+    j = best_end
+    while i > 0:
+        # Check for a diagonal move (match or substitution)
+        if j > 0 and dp[i, j] == dp[i - 1, j - 1] + (0 if candidate_arr[i - 1] == text_arr[j - 1] else 1):
+            i -= 1
+            j -= 1
+        elif dp[i, j] == dp[i - 1, j] + 1:
+            i -= 1
+        else:
+            j -= 1
+    return j  # start index in text
+
+
+def find_matching_content(candidate_text: str, tex_content: str, sim_threshold: float) -> Optional[str]:
+    """
+    Find the substring of tex_content that most closely matches candidate_text using
+    dynamic programming accelerated by numba. Returns the matching substring if its
+    normalized similarity (1 - (edit_distance / len(candidate_text))) is above sim_threshold,
+    otherwise returns None.
+    """
+    candidate_norm = normalize_text(candidate_text)
+    tex_norm = normalize_text(tex_content)
+    
+    m = len(candidate_norm)
+    n = len(tex_norm)
+    if m == 0 or n == 0:
+        return None
+
+    # Convert strings to numpy arrays of integer character codes.
+    candidate_arr = np.empty(m, dtype=np.int32)
+    for i, c in enumerate(candidate_norm):
+        candidate_arr[i] = ord(c)
+    text_arr = np.empty(n, dtype=np.int32)
+    for j, c in enumerate(tex_norm):
+        text_arr[j] = ord(c)
+    
+    dp = compute_dp(candidate_arr, text_arr)
+    best_end, min_distance = find_best_end(dp, m, n)
+    similarity = (m - min_distance) / m
+
+    logging.info("Similarity: %.3f", similarity)
+    if similarity < sim_threshold:
+        return None
+    start_index = backtrack(dp, candidate_arr, text_arr, m, best_end)
+    return tex_norm[start_index:best_end]
+
+
+def parse_candidate_filename(filename: str) -> Optional[Tuple[str, int]]:
+    """
+    Parse candidate filename in the format: [tex file basename]_pg[pagenum]_repeat1.md
+    Returns tuple (tex_basename, page_num) or None if the format doesn't match.
+    """
+    basename = os.path.basename(filename)
+    match = re.match(r"(.+)_pg(\d+)_repeat\d+\.md$", basename)
+    if match:
+        tex_basename = match.group(1)
+        page_num = int(match.group(2))
+        return tex_basename, page_num
+    return None
+
+
+def validate_equation(equation: str) -> bool:
+    """
+    Validate that an equation renders correctly with KaTeX.
+    Returns True if the equation is valid, False otherwise.
+    """
+    rendered = render_equation(equation)
+    return rendered is not None
+
+
+def process_candidate_file(candidate_file: str, pdfs_folder: str, sim_threshold: float) -> List[MathTest]:
+    """
+    Process a single candidate file.
+    Returns a list of MathTest objects extracted from the corresponding TeX file.
+    """
+    logging.info("Processing %s", candidate_file)
+    tests = []
+    parse_result = parse_candidate_filename(candidate_file)
+    if not parse_result:
+        logging.error("Filename %s does not match expected format.", candidate_file)
+        return tests
+
+    tex_basename, page_num = parse_result
+    tex_file_path = os.path.join(pdfs_folder, f"{tex_basename}.tex")
+    
+    if not os.path.exists(tex_file_path):
+        logging.error("TeX file %s not found for candidate %s.", tex_file_path, candidate_file)
+        return tests
+    
+    candidate_text = extract_candidate_content(candidate_file)
+    tex_content = extract_tex_content(tex_file_path)
+    if not tex_content:
+        logging.error("No content extracted from %s", tex_file_path)
+        return tests
+
+    matching_tex = find_matching_content(candidate_text, tex_content, sim_threshold)
+    if not matching_tex:
+        logging.warning("No matching TeX content found in %s for candidate %s", tex_file_path, candidate_file)
+        return tests
+
+    logging.debug("Matching TeX content: %s", matching_tex)
+
+    math_equations = extract_math_from_tex(matching_tex)
+    if not math_equations:
+        logging.warning("No math equations found in matching content for candidate %s", candidate_file)
+        return tests
+
+    # Filter out equations that are too short, remove duplicates, and shuffle
+    math_equations = [(eq_type, eq.strip()) for (eq_type, eq) in math_equations if len(eq.strip()) > 20]
+    math_equations = list(set(math_equations))
+    random.shuffle(math_equations)
+
+    for i, (eq_type, equation) in enumerate(math_equations):
+        if validate_equation(equation):
+            test_id = f"{tex_basename}_pg{page_num}_math_{i:03d}"
+            math_test = MathTest(
+                id=test_id,
+                pdf=f"{tex_basename}.pdf",
+                page=page_num,
+                type="math",
+                math=equation,
+            )
+            tests.append(math_test)
+            if len(tests) >= 10:
+                break
+
+    return tests
+
+
+def process_tex_file_group(tex_basename: str, candidate_files: List[str], pdfs_folder: str,
+                           sim_threshold: float, max_pages: int) -> List[MathTest]:
+    """
+    For a given TeX file, group candidate files by page, randomly shuffle the pages,
+    and process them one-by-one. Stop once max_pages (pages with valid equations) have
+    been processed.
+    """
+    tests = []
+    valid_pages = set()
+
+    # Group candidate files by page number.
+    page_dict: Dict[int, List[str]] = {}
+    for candidate_file in candidate_files:
+        parse_result = parse_candidate_filename(candidate_file)
+        if not parse_result:
+            continue
+        _, page_num = parse_result
+        page_dict.setdefault(page_num, []).append(candidate_file)
+    
+    # For each page, randomly choose one candidate file.
+    distinct_candidate_files = []
+    for page_num, files in page_dict.items():
+        chosen_file = random.choice(files)
+        distinct_candidate_files.append(chosen_file)
+    
+    # Shuffle the pages randomly.
+    random.shuffle(distinct_candidate_files)
+    
+    # Process pages sequentially until max_pages with valid equations have been found.
+    for candidate_file in distinct_candidate_files:
+        result = process_candidate_file(candidate_file, pdfs_folder, sim_threshold)
+        if result:
+            tests.extend(result)
+            # Mark this page as valid.
+            page_num = parse_candidate_filename(candidate_file)[1]
+            valid_pages.add(page_num)
+            if len(valid_pages) >= max_pages:
+                break
+
+    return tests
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Extract math equations from candidate files and corresponding TeX bases."
+    )
+    parser.add_argument("--math_data", required=True, help="Path to math_data folder")
+    parser.add_argument("--candidate", required=True, help="Candidate folder name inside math_data")
+    parser.add_argument("--max_pages", type=int, default=3, help="Maximum distinct pages with equations to process per TeX document")
+    parser.add_argument("--parallel", type=int, default=8, help="Maximum process pool workers")
+    parser.add_argument("--sim_threshold", type=float, default=0.7, help="Similarity threshold for matching candidate text")
+    
+    args = parser.parse_args()
+    
+    candidate_folder = os.path.join(args.math_data, args.candidate)
+    pdfs_folder = os.path.join(args.math_data, "pdfs")
+    
+    candidate_files = glob.glob(os.path.join(candidate_folder, "*.md"))
+    logging.info("Found %d candidate files.", len(candidate_files))
+    
+    # Group candidate files by TeX basename.
+    tex_groups: Dict[str, List[str]] = {}
+    for candidate_file in candidate_files:
+        parse_result = parse_candidate_filename(candidate_file)
+        if not parse_result:
+            continue
+        tex_basename, _ = parse_result
+        tex_groups.setdefault(tex_basename, []).append(candidate_file)
+    logging.info("Found %d TeX groups.", len(tex_groups))
+    
+    # Remove output file if it exists to start fresh
+    output_file = os.path.join(args.math_data, "math_tests.jsonl")
+    if os.path.exists(output_file):
+        os.remove(output_file)
+    
+    all_math_tests = []
+    
+    # Process each TeX group in parallel using ProcessPoolExecutor.
+    with ProcessPoolExecutor(max_workers=args.parallel) as executor:
+        future_to_tex = {
+            executor.submit(process_tex_file_group, tex_basename, candidate_list, pdfs_folder,
+                            args.sim_threshold, args.max_pages): tex_basename
+            for tex_basename, candidate_list in tex_groups.items()
+        }
+        for future in tqdm(as_completed(future_to_tex), total=len(future_to_tex), desc="Processing TeX files"):
+            tex_basename = future_to_tex[future]
+            try:
+                tests = future.result()
+                all_math_tests.extend(tests)
+                # Incrementally save tests as each TeX group finishes processing.
+                save_tests(all_math_tests, output_file)
+            except Exception as e:
+                logging.error("Error processing TeX group %s: %s", tex_basename, e)
+    
+    logging.info("Found %d valid math equations from %d TeX groups.", len(all_math_tests), len(tex_groups))
+    logging.info("Results incrementally saved to %s", output_file)
+
+
+if __name__ == "__main__":
+    main()

olmocr/bench/prompts.py

+import re
+from dataclasses import dataclass
+from typing import Optional
+
+
+def claude_response_format_schema() -> dict:
+    return (
+        {
+            "name": "page_response",
+            "description": "Extracts text from pdf's.",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "primary_language": {
+                        "type": ["string", "null"],
+                        "description": "The primary language of the text using two-letter codes or null if there is no text at all that you think you should read.",
+                    },
+                    "is_rotation_valid": {
+                        "type": "boolean",
+                        "description": "Is this page oriented correctly for reading? Answer only considering the textual content, do not factor in the rotation of any charts, tables, drawings, or figures.",
+                    },
+                    "rotation_correction": {
+                        "type": "integer",
+                        "description": "Indicates the degree of clockwise rotation needed if the page is not oriented correctly.",
+                        "enum": [0, 90, 180, 270],
+                        "default": 0,
+                    },
+                    "is_table": {
+                        "type": "boolean",
+                        "description": "Indicates if the majority of the page content is in tabular format.",
+                    },
+                    "is_diagram": {
+                        "type": "boolean",
+                        "description": "Indicates if the majority of the page content is a visual diagram.",
+                    },
+                    "natural_text": {
+                        "type": ["string", "null"],
+                        "description": "The natural text content extracted from the page.",
+                    },
+                },
+                "required": [
+                    "primary_language",
+                    "is_rotation_valid",
+                    "rotation_correction",
+                    "is_table",
+                    "is_diagram",
+                    "natural_text",
+                ],
+            },
+        },
+    )
+
+
+def gemini_response_format_schema() -> dict:
+    return (
+        {
+            "type": "OBJECT",
+            "properties": {
+                "primary_language": {
+                    "type": "STRING",
+                    "description": "The primary language of the text using two-letter codes or null if there is no text at all that you think you should read.",
+                },
+                "is_rotation_valid": {
+                    "type": "BOOL",
+                    "description": "Is this page oriented correctly for reading? Answer only considering the textual content, do not factor in the rotation of any charts, tables, drawings, or figures.",
+                },
+                "rotation_correction": {
+                    "type": "INTEGER",
+                    "enum": [0, 90, 180, 270],
+                    "description": "Indicates the degree of clockwise rotation needed if the page is not oriented correctly.",
+                },
+                "is_table": {"type": "BOOL", "description": "Indicates if the majority of the page content is in tabular format."},
+                "is_diagram": {"type": "BOOL", "description": "Indicates if the majority of the page content is a visual diagram."},
+                "natural_text": {"type": "STRING", "description": "The natural text content extracted from the page."},
+            },
+            "required": ["primary_language", "is_rotation_valid", "rotation_correction", "is_table", "is_diagram", "natural_text"],
+            "propertyOrdering": ["primary_language", "is_rotation_valid", "rotation_correction", "is_table", "is_diagram", "natural_text"],
+        },
+    )
+
+
+def build_find_difference_prompt(base_text: str) -> str:
+    return (
+        f"Below is an image of a document page, along with raw textual content previously extracted using different models."
+        f"Your goal is to carefully identify the differences between the extracted texts from both models and determine which one is more accurate by comparing them with the image."
+        f"Only return the differences and specify which model extracted the text with higher accuracy.\n"
+        f"Do not hallucinate.\n"
+        f"RAW_TEXT_START\n{base_text}\nRAW_TEXT_END"
+    )
+

olmocr/bench/runners/run_chatgpt.py

+import json
+import os
+
+from openai import OpenAI
+
+from olmocr.data.renderpdf import render_pdf_to_base64png
+from olmocr.prompts.anchor import get_anchor_text
+from olmocr.prompts.prompts import (
+    PageResponse,
+    build_openai_silver_data_prompt,
+    openai_response_format_schema,
+)
+
+def run_chatgpt(pdf_path: str, page_num: int = 1, model: str = "gpt-4o-2024-08-06", temperature: float = 0.1) -> str:
+    """
+    Convert page of a PDF file to markdown using the commercial openAI APIs.
+
+    See run_server.py for running against an openai compatible server
+
+    Args:
+        pdf_path (str): The local path to the PDF file.
+
+    Returns:
+        str: The OCR result in markdown format.
+    """
+    # Convert the first page of the PDF to a base64-encoded PNG image.
+    image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=2048)
+    anchor_text = get_anchor_text(pdf_path, page_num, pdf_engine="pdfreport")
+
+    if not os.getenv("OPENAI_API_KEY"):
+        raise SystemExit("You must specify an OPENAI_API_KEY")
+
+    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+    response = client.chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
+                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
+                ],
+            }
+        ],
+        temperature=temperature,
+        max_tokens=3000,
+        response_format=openai_response_format_schema(),
+    )
+
+    raw_response = response.choices[0].message.content
+
+    assert len(response.choices) > 0
+    assert response.choices[0].message.refusal is None
+    assert response.choices[0].finish_reason == "stop"
+
+    data = json.loads(raw_response)
+    data = PageResponse(**data)
+
+    return data.natural_text

olmocr/bench/runners/run_claude.py

+import json
+import os
+
+from anthropic import Anthropic
+from prompts import (
+    build_openai_silver_data_prompt,
+    claude_response_format_schema,
+)
+
+from olmocr.data.renderpdf import render_pdf_to_base64png
+from olmocr.prompts.anchor import get_anchor_text
+
+
+def run_claude(pdf_path: str, page_num: int = 1, model: str = "claude-3-7-sonnet-20250219", temperature: float = 0.1) -> str:
+    """
+    Convert page of a PDF file to markdown using Claude OCR.
+    This function renders the specified page of the PDF to an image, runs OCR on that image,
+    and returns the OCR result as a markdown-formatted string.
+
+    Args:
+        pdf_path (str): The local path to the PDF file.
+        page_num (int): The page number to process (starting from 1).
+        model (str): The Claude model to use.
+        temperature (float): The temperature parameter for generation.
+
+    Returns:
+        str: The OCR result in markdown format.
+    """
+
+    if not os.getenv("ANTHROPIC_API_KEY"):
+        raise SystemExit("You must specify an ANTHROPIC_API_KEY")
+
+    image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=2048)
+    anchor_text = get_anchor_text(pdf_path, page_num, pdf_engine="pdfreport")
+    client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
+    response = client.messages.create(
+        model=model,
+        max_tokens=3000,
+        temperature=temperature,
+        # system=system_prompt,
+        tools=claude_response_format_schema(),
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_base64}},
+                    {
+                        "type": "text",
+                        "text": f"{build_openai_silver_data_prompt(anchor_text)}. Use the page_response tool to respond. If the propeties are true, then extract the text from them and respond in natural_text.",
+                    },
+                ],
+            }
+        ],
+    )
+
+    json_sentiment = None
+    for content in response.content:
+        if content.type == "tool_use" and content.name == "page_response":
+            json_sentiment = content.input
+            break
+
+    if json_sentiment:
+        response = json.dumps(json_sentiment, indent=2)
+        return response

olmocr/bench/runners/run_gemini.py

+import base64
+import os
+
+from google.ai import generativelanguage as glm
+from google.api_core import client_options
+
+
+from olmocr.data.renderpdf import render_pdf_to_base64png
+from olmocr.prompts.anchor import get_anchor_text
+from olmocr.prompts.prompts import build_openai_silver_data_prompt
+
+def run_gemini(pdf_path: str, page_num: int = 1, model: str = "gemini-2.0-flash", temperature: float = 0.1) -> str:
+    """
+    Convert page of a PDF file to markdown using Gemini's vision capabilities.
+    This function renders the specified page of the PDF to an image, runs OCR on that image,
+    and returns the OCR result as a markdown-formatted string.
+
+    Args:
+        pdf_path (str): The local path to the PDF file.
+        page_num (int): The page number to process (starting from 1).
+        model (str): The Gemini model to use.
+        temperature (float): The temperature parameter for generation.
+
+    Returns:
+        str: The OCR result in markdown format.
+    """
+    if not os.getenv("GEMINI_API_KEY"):
+        raise SystemExit("You must specify an GEMINI_API_KEY")
+
+    image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=2048)
+    anchor_text = get_anchor_text(pdf_path, page_num, pdf_engine="pdfreport")
+    api_key = os.getenv("GEMINI_API_KEY")
+    client = glm.GenerativeServiceClient(
+        client_options=client_options.ClientOptions(
+            api_key=api_key,
+        ),
+    )
+
+    image_part = glm.Part(inline_data=glm.Blob(mime_type="image/png", data=base64.b64decode(image_base64)))
+
+    text_part = glm.Part(text=f"""{build_openai_silver_data_prompt(anchor_text)}""")
+    generation_config = glm.GenerationConfig(
+        temperature=temperature,
+        top_p=1.0,
+        top_k=32,
+        max_output_tokens=4096,
+    )
+    # response_schema = gemini_response_format_schema()
+    request = glm.GenerateContentRequest(
+        model=f"models/{model}",
+        contents=[glm.Content(parts=[image_part, text_part])],
+        generation_config=generation_config,
+    )
+
+    # request = glm.GenerateContentRequest(
+    #     model=f"models/{model}",
+    #     contents=[glm.Content(parts=[image_part, text_part])],
+    #     generation_config=generation_config,
+    #     tools=[
+    #         glm.Tool(
+    #             function_declarations=[
+    #                 glm.FunctionDeclaration(
+    #                     name="page_response",
+    #                     parameters=response_schema
+    #                 )
+    #             ]
+    #         )
+    #     ],
+    #     tool_config=glm.ToolConfig(
+    #         function_calling_config=glm.FunctionCallingConfig(
+    #             mode="any",
+    #             allowed_function_names=["page_response"]
+    #         )
+    #     )
+    # )
+
+    response = client.generate_content(request)
+
+    assert len(response.candidates) > 0, "No candidates found"
+    assert response.candidates[0].finish_reason == glm.Candidate.FinishReason.STOP, "Finish reason was not STOP, likely a processing error or repetition failure"
+
+    result = response.candidates[0].content.parts[0].text
+    return result

olmocr/bench/runners/run_gotocr.py

+import base64
+import os
+import tempfile
+
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+from olmocr.data.renderpdf import render_pdf_to_base64png
+
+# Global cache for the model and tokenizer.
+_device = "cuda" if torch.cuda.is_available() else "cpu"
+_model = None
+_tokenizer = None
+
+
+def load_model():
+    """
+    Load the GOT-OCR model and tokenizer if they haven't been loaded already.
+    Returns:
+        model: The GOT-OCR model loaded on the appropriate device.
+        tokenizer: The corresponding tokenizer.
+    """
+    global _model, _tokenizer
+    if _model is None or _tokenizer is None:
+        _tokenizer = AutoTokenizer.from_pretrained("ucaslcl/GOT-OCR2_0", trust_remote_code=True)
+        _model = AutoModel.from_pretrained(
+            "ucaslcl/GOT-OCR2_0",
+            trust_remote_code=True,
+            use_safetensors=True,
+            revision="979938bf89ccdc949c0131ddd3841e24578a4742",
+            pad_token_id=_tokenizer.eos_token_id,
+        )
+        _model = _model.eval().to(_device)
+    return _model, _tokenizer
+
+
+def run_gotocr(pdf_path: str, page_num: int = 1, ocr_type: str = "ocr") -> str:
+    """
+    Convert page of a PDF file to markdown using GOT-OCR.
+
+    This function renders the first page of the PDF to an image, runs OCR on that image,
+    and returns the OCR result as a markdown-formatted string.
+
+    Args:
+        pdf_path (str): The local path to the PDF file.
+
+    Returns:
+        str: The OCR result in markdown format.
+    """
+    # Ensure the model is loaded (cached across calls)
+    model, tokenizer = load_model()
+
+    # Convert the first page of the PDF to a base64-encoded PNG image.
+    base64image = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=1024)
+
+    # Write the image to a temporary file.
+    with tempfile.NamedTemporaryFile("wb", suffix=".png", delete=False) as tmp:
+        tmp.write(base64.b64decode(base64image))
+        tmp_filename = tmp.name
+
+    # Run GOT-OCR on the saved image.
+    result = model.chat(tokenizer, tmp_filename, ocr_type=ocr_type)
+
+    # Clean up the temporary file.
+    os.remove(tmp_filename)
+
+    return result