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

- Move __use_inside_model__ and __model_mode__ from operators/__init__.py to model/__init__.py
- These variables are more appropriately located in the model module since they relate to model configuration

- 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

- Add initial setup for layout detection
- Implement conditional cropping for tall images
- Skip cropping for wide images to improve performance
- Reuse Image object across layout detection steps

- Implement image cropping and pasting technique to enhance layout detection
- Adjust detected polygons to original image coordinates
- Add comments for better code readability

- 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

- Import paddle module and disable its signal handler to prevent interference with other components
- This change addresses potential conflicts between PaddlePaddle and other libraries or system signals

- Add environment variables to disable albumentations and yolo updates
- Import torchtext and disable deprecation warnings
- Update unimernet to 0.2.2
- Specify ultralytics version as >=8.3.48
- Remove upper version limit for torch

- 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

- Remove usage of AtomModelSingleton for OCR model creation
- Add ocr_model_init function to initialize OCR model
- Update OCR model initialization in pdf_extract_kit.py and pdf_parse_union_core_v2.py
- Modify txt_spans_extract_v2 function to accept ocr_model as a parameter
- Update parse_page_core function to use ocr_model instead of lang for OCR processing

- 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 threading.Lock import and usage
- Delete unused model initialization comments and code- Simplify OCR model initialization in both pdf_extract_kit.py and pdf_parse_union_core_v2.py

- Remove usage of AtomModelSingleton for OCR model initialization
- Add import of ocr_model_init from model_init module
- Update OCR model initialization process to use ocr_model_init function
- Remove lock for OCR processing as it's no longer needed

- Remove usage of ModelSingleton class
- Initialize model directly using custom_model_init function
- Add self._lock attribute to PDFExtractKit class for thread safety- Replace local lock with self._lock for OCR processing

- 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