GitLab

fix absence of sorted_boxes, merge_det_boxes, update_det_boxes.

- Remove OCR engine instantiation inside the loop
- Pass language directly to the table model instead of OCR engine
- Simplify code structure and improve readability

- Update batch processing logic for improved efficiency
- Refactor image analysis and inference methods
- Optimize dataset handling and image retrieval
- Improve error handling and logging in batch processes

- Comment out the line that updates det_count in batch_analyze.py
- Add a new OCR model configuration for Chinese (ch_lite) in models_config.yml- Update the Chinese OCR model configuration to use a different recognition model

- Add functions to calculate IoU, check if tables are inside each other, and merge tables
- Implement table merging for high IoU tables
- Add filtering to remove nested tables that don't overlap but cover a large area
- Update table_res_list and layout_res to reflect these changes

- Update OCR score formatting in batch_analyze.py and pdf_parse_union_core_v2.py
- Change score rounding method to preserve three decimal places
- Enhance accuracy representation without significantly altering the score value

- Update table recognition logic to process each table individually
- Refactor layout detection to use tqdm for progress tracking
- Optimize OCR recognition by using a single tqdm wrapper
- Improve MFR prediction with a more accurate progress bar
- Simplify MFD prediction by removing unnecessary total calculation

- Remove unused imports and comments
- Increase MIN_BATCH_INFERENCE_SIZE from 100 to 200
- Comment out VRAM cleaning and logging in batch_analyze.py
- Simplify code in doc_analyze_by_custom_model.py- Add tqdm progress bar in pdf_parse_union_core_v2.py
- Enable tqdm in OCR processing

- Add tqdm progress bar to batch prediction loops in multiple model modules
- Improve logging and error handling in batch analysis script
- Update table model initialization to use default sub-model if none specified
- Add tqdm dependency to requirements.txt

- Replace direct OCR model access with AtomModelSingleton for better model management
- Round OCR scores to 2 decimal places for consistency
- Improve error handling and logging in batch analysis
- Simplify OCR result processing in pdf_parse_union_core_v2.py

- 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

- 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

- Reduce YOLO_LAYOUT_BASE_BATCH_SIZE from 4 to 1
- Simplify batch ratio calculation for formula detection
- Remove unused conditional logic in batch ratio determination

- Modify the batch analyze process to handle the rapid table model's output
- Add logic_points variable to capture additional output from rapid table prediction

- Add support for NPU (Neural Processing Unit) when available
- Implement batch analysis for GPU and NPU devices
- Optimize memory usage and improve performance
- Update logging and error handling

- 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