GitLab

- Add support for multiple languages in OCR processing
- Create separate lists for each language to improve processing efficiency
- Update OCR model initialization to use PytorchPaddleOCR instead of ModifiedPaddleOCR
- Modify get_ocr_result_list function to include language information- Improve logging for OCR detection and recognition

- Split OCR process into detection and recognition stages
- Update batch analysis and document analysis pipelines
- Modify OCR result formatting and handling
- Remove unused imports and optimize code structure

- Add base model structure for OCR in pytorch
- Implement data augmentation and transformation modules
- Create utilities for dictionary handling and state dict conversion
- Include post-processing modules for OCR
- Add weight initialization and loading functions

- Comment out margin cropping to prevent errors with broken files
- Refactor image resizing to preserve aspect ratio
- Update padding calculation and application using OpenCV

- Replace deprecated importlib.resources.path with importlib.resources.files
- Simplify code structure and improve readability
- Remove unnecessary comments and empty lines

- Comment out LayoutLMv3, TableMaster, and StructEqTable models
- Update MFR model path to unimernet_hf_small_2503- Remove unused import in Unimernet.py

- Remove half() calls for DocLayoutYOLO and YOLOv8 models
- This change prevents potential errors when running models on CPU

- Update config version to1.2.0
- Refactor model initialization in model_init.py- Update dependencies in requirements.txt files
- Remove unused imports and models
- Add conditional imports for table models

- Add support for Apple M1 chips (mps device)
- Refactor image processing for better performance and compatibility
- Update model loading and inference for various devices
- Adjust batch processing and memory management

- Add UnimerMBartConfig and UnimerSwinConfig classes
- Implement UnimerSwinImageProcessor for image preprocessing- Create necessary __init__.py files for module structure

- Remove unnecessary __getitem__ method
- Simplify image cropping in detect_math_formula_region
- Improve code readability and efficiency

- Remove unused @ImportPIL decorator from load_images_from_pdf function
- Update image shape handling in YOLOv11.py for better compatibility

These changes improve code readability and performance without altering the original functionality.

- Replace PIL with cv2 for image processing
- Fix issues with image cropping and resizing
- Add boundary checks and error handling
- Optimize code for better performance and readability

- Remove PIL usage across multiple files
- Convert image processing functions to use NumPy arrays
- Update crop_img function to work with NumPy arrays
- Modify image loading and resizing to use NumPy and OpenCV
- Clean up unused imports and comments related to PIL

- Sort detected images by area before processing to enhance MFR accuracy
- Implement stable sorting to maintain original order of images with equal

- Add license ID information to the log for better traceability
- Improve logging format to include both license ID and expiration date

- Add ImportError handling to silence known import-related exceptions
- Improve generic exception handling to log error messages- Maintain existing specific exception handlers for license-related issues

- Add license verification logic for Ascend plugin
- Handle different license-related exceptions with appropriate error messages
- Log success message with license expiration date if verification passes
- Fall back to CPU model if license verification fails or plugin is not available

- Remove redundant imports for StructTableModel and TableMasterPaddleModel
- Reorder imports to group related modules together
- Update import structure for better readability and maintainability

- Remove unused utility functions
- Update import statements for better readability
- Add conditional imports for Ascend plugin
- Refactor table model initialization to support NPU

- Increase batch size from 8 to 256 for language detection inference
- Add timing measurement for language detection process

refactor(magic_pdf): adjust VRAM allocation and MFR batch size- Update VRAM allocation logic to use 'VIRTUAL_VRAM_SIZE' environment variable
- Reduce MFR (Math Formula Recognition) batch size from 64 to 32

- Add key length validation for ONNX model initialization
- Move import statements to the top of the file
- Wrap model initialization in a try-except block for better error handling
- Refactor code to improve readability and maintainability

- Import get_device function from magic_pdf.libs.config_reader- Update RapidTableModel initialization to include device parameter for Unitable model

- Update RapidTable dependency to version 1.0.3
- Add support for sub-models in RapidTable
- Update magic-pdf configuration to include table sub-model
- Modify table model initialization to support sub-models
- Update table prediction logic to handle new output format

- Merge title blocks that are close to each other horizontally
- Adjust line insertion logic for title blocks- Increase image size and decrease confidence threshold for layout detection
- Update DocLayoutYOLO model weights
- Refactor drawing of bounding boxes for different block types

- Remove conditional logic for OCR engine selection
- Always use RapidOCR as the OCR engine
- Simplify the __init__ method by removing unused code

- Remove YOLO v11 language detection model from model_configs.yaml
- Update language detection utils to use a fixed model path instead of dynamic configuration
- Remove unused model weight parameter for YOLO v11 language detection

- 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