GitLab

- Remove LangDetectMode and related conditional logic
- Use a single model weight for language detection
- Add logging for language detection results
- Update model initialization and prediction methods

- Add language detection model initialization and integration
- Update model list to include language detection
- Refactor language detection utils for better model management

- Add separate models for Chinese/Japanese and English/French/German detection
- Implement mode-based detection to use appropriate models for different languages
- Update language detection process to use higher DPI for better accuracy
- Modify model initialization and prediction logic to support new language-specific models

- Add check for empty OCR result when using PaddleOCR model
- Assign None to ocr_result if no text is detected, preventing further errors

- Add missing 'else' statement in OCR model selection logic
- Ensure consistent formatting of 'if' statements for better readability
- Remove unnecessary empty line in the 'app.py' file

- Remove logger.info() call for additional_ocr_params to reduce log verbosity

- Implement ONNXModelSingleton to manage ONNX models
- Modify ModifiedPaddleOCR to use ONNX models on ARM CPUs without CUDA
- Update RapidTableModel to use RapidOCR with ONNXRuntime on CPU
- Add rapidocr_onnxruntime dependency in setup.py

- Remove use_npu variable initialization
- Comment out device assignment and npu check
- Comment out use_npu parameter in ModifiedPaddleOCR constructor

- Update `clean_memory.py` to use `torch_npu.npu` instead of `torch.npu`
- Update `model_utils.py` to use `torch_npu.npu` instead of `torch.npu`
- Simplify NPU availability check and bfloat16 support in `pdf_parse_union_core_v2.py`

- Update clean_memory function to support both CUDA and NPU devices
- Implement get_device function to centralize device selection logic
- Modify model initialization and memory cleaning to use the selected device
- Update RapidTableModel to support both RapidOCR and PaddleOCR engines

- Add NPU support for memory cleaning and model initialization
- Optimize table model initialization and prediction process
- Update memory utils to support NPU
- Add language parameter for table model

- Add YOLOv11 language detection model for PDF documents
- Implement language detection in PymuDocDataset
- Update app.py to include 'auto' language option
- Create language detection utilities and constants

- Remove YOLO_VERBOSE environment variable from multiple files
- Set verbose=False in YOLOv8 prediction method to suppress logger output

- Change import paths from paddleocr.ppocr to ppocr for utility functions
- Update import paths for logging and utility modules in ppocr_273_mod.py- Modify import paths for tablemaster_paddle.py to use ppstructure instead of paddleocr.ppstructure

- Change import path for TableSystem from 'ppstructure.table.predict_table' to 'paddleocr.ppstructure.table.predict_table'
- Change import path for init_args from 'ppstructure.utility' to 'paddleocr.ppstructure.utility'

- Modify import paths for paddleocr utilities in ocr_utils.py and ppocr_273_mod.py
- Change from `ppocr.utils.utility` to `paddleocr.ppocr.utils.utility`
- Update related import statements in two files to reflect the new path

- Remove concurrency limit logic from app.py
- Update model initialization process in various modules
- Remove unused VRAM check for concurrency limit
- Refactor OCR model initialization in pdf_extract_kit.py
- Update txt_spans_extract_v2 function to use lang parameter instead of ocr_model

- Add threading support for OCR model initialization
- Modify AtomModelSingleton to handle thread-specific instances
- Update PDFExtractKit and PDFParseUnionCoreV2 to use new thread-safe OCR initialization

- Remove unnecessary threading.Lock in AtomModelSingleton
- Add threading.Lock to CustomPEKModel for OCR processing
- Simplify model initialization logic in AtomModelSingleton

- Add condition to return existing model if already initialized
- Improve efficiency by avoiding redundant model creation

- Introduce a lock to synchronize access to OCR model initialization- This change improves thread safety when multiple threads access the OCR model concurrently
- The lock ensures that the OCR model is initialized only once, even in multi-threaded scenarios

- Update VRAM checking logic in app.py and model_utils.py
- Add None and type checks for VRAM values
- Adjust concurrency limit calculation in app.py
- Modify clean_vram function to handle cases with no VRAM information

- Add get_concurrency_limit function to calculate concurrency limit based on VRAM
- Update clean_vram function and rename to get_vram for better clarity
- Apply concurrency limit to the to_markdown function in the Gradio app

- Remove unused function `calculate_angle_degrees`- Refactor `calculate_is_angle` to use directly in OCR processing
- Eliminate unnecessary loop index `idx` in OCR processing loops

- Add confidence score threshold to filter out low confidence OCR results
- Improve OCR accuracy by ignoring less certain detections

- Add a null check for OCR result in the predict method
- Return None values if OCR result is None to prevent further processing

- Implement new text extraction method (txt_spans_extract_v2) to enhance accuracy
- Add character filling in spans for better text reconstruction
- Introduce empty span handling using OCR for missed text
- Optimize span filtering and overlap removal

- Update OCR utils to handle different box formats and improve angle calculation
- Modify PDF extraction kit to support OCR option and optimize processing flow
- Enhance PPOCR model to sort and filter detection boxes, improving text splitting accuracy

- Add calculate_is_angle function to detect angled text boxes
- Update update_det_boxes and merge_det_boxes functions to handle angled text boxes
- Modify angle detection logic in various parts of the code