517 lines
20 KiB
Python
517 lines
20 KiB
Python
"""
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Tool_OCR - Core OCR Service
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PaddleOCR-VL integration for text and structure extraction
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"""
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import json
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import logging
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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from datetime import datetime
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import uuid
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from paddleocr import PaddleOCR, PPStructureV3
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from PIL import Image
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from pdf2image import convert_from_path
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from app.core.config import settings
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from app.services.office_converter import OfficeConverter, OfficeConverterError
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logger = logging.getLogger(__name__)
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class OCRService:
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"""
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Core OCR service using PaddleOCR-VL
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Handles text recognition and document structure analysis
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"""
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def __init__(self):
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"""Initialize PaddleOCR and PPStructure engines"""
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self.ocr_languages = settings.ocr_languages_list
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self.confidence_threshold = settings.ocr_confidence_threshold
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# Initialize PaddleOCR engine (will be lazy-loaded per language)
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self.ocr_engines = {}
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# Initialize PP-Structure for layout analysis
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self.structure_engine = None
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# Initialize Office document converter
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self.office_converter = OfficeConverter()
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logger.info("OCR Service initialized")
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def get_ocr_engine(self, lang: str = 'ch') -> PaddleOCR:
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"""
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Get or create OCR engine for specified language
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Args:
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lang: Language code (ch, en, japan, korean, etc.)
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Returns:
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PaddleOCR engine instance
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"""
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if lang not in self.ocr_engines:
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logger.info(f"Initializing PaddleOCR engine for language: {lang}")
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self.ocr_engines[lang] = PaddleOCR(
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use_angle_cls=True,
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lang=lang,
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# Note: show_log and use_gpu parameters removed in PaddleOCR 3.x
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)
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logger.info(f"PaddleOCR engine ready for {lang}")
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return self.ocr_engines[lang]
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def get_structure_engine(self) -> PPStructureV3:
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"""
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Get or create PP-Structure engine for layout analysis
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Returns:
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PPStructure engine instance
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"""
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if self.structure_engine is None:
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logger.info("Initializing PP-StructureV3 engine")
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self.structure_engine = PPStructureV3(
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use_doc_orientation_classify=False,
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use_doc_unwarping=False,
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use_textline_orientation=False,
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use_table_recognition=True,
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use_formula_recognition=True,
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layout_threshold=0.5,
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)
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logger.info("PP-StructureV3 engine ready")
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return self.structure_engine
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def convert_pdf_to_images(self, pdf_path: Path, output_dir: Path) -> List[Path]:
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"""
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Convert PDF to images (one per page)
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Args:
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pdf_path: Path to PDF file
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output_dir: Directory to save converted images
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Returns:
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List of paths to converted images
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"""
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try:
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output_dir.mkdir(parents=True, exist_ok=True)
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logger.info(f"Converting PDF {pdf_path.name} to images")
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# Convert PDF to images (300 DPI for good quality)
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images = convert_from_path(
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str(pdf_path),
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dpi=300,
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fmt='png'
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)
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image_paths = []
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for i, image in enumerate(images):
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# Save each page as PNG
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image_path = output_dir / f"{pdf_path.stem}_page_{i+1}.png"
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image.save(str(image_path), 'PNG')
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image_paths.append(image_path)
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logger.info(f"Saved page {i+1} to {image_path.name}")
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logger.info(f"Converted {len(image_paths)} pages from PDF")
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return image_paths
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except Exception as e:
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logger.error(f"PDF conversion error: {str(e)}")
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raise
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def process_image(
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self,
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image_path: Path,
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lang: str = 'ch',
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detect_layout: bool = True,
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confidence_threshold: Optional[float] = None
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) -> Dict:
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"""
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Process single image with OCR and layout analysis
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Args:
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image_path: Path to image file
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lang: Language for OCR
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detect_layout: Whether to perform layout analysis
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confidence_threshold: Minimum confidence threshold (uses default if None)
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Returns:
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Dictionary with OCR results and metadata
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"""
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start_time = datetime.now()
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threshold = confidence_threshold if confidence_threshold is not None else self.confidence_threshold
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try:
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# Check if file is Office document
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if self.office_converter.is_office_document(image_path):
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logger.info(f"Detected Office document: {image_path.name}, converting to PDF")
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try:
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# Convert Office document to PDF
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pdf_path = self.office_converter.convert_to_pdf(image_path)
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logger.info(f"Office document converted to PDF: {pdf_path.name}")
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# Process the PDF (will be handled by PDF processing logic below)
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image_path = pdf_path
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except OfficeConverterError as e:
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logger.error(f"Office conversion failed: {str(e)}")
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raise
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# Check if file is PDF
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is_pdf = image_path.suffix.lower() == '.pdf'
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if is_pdf:
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# Convert PDF to images
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logger.info(f"Detected PDF file: {image_path.name}, converting to images")
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pdf_images_dir = image_path.parent / f"{image_path.stem}_pages"
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image_paths = self.convert_pdf_to_images(image_path, pdf_images_dir)
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# Process all pages
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all_text_regions = []
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total_confidence_sum = 0.0
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total_valid_regions = 0
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all_layout_data = []
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all_images_metadata = []
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for page_num, page_image_path in enumerate(image_paths, 1):
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logger.info(f"Processing PDF page {page_num}/{len(image_paths)}")
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# Process each page
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page_result = self.process_image(
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page_image_path,
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lang=lang,
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detect_layout=detect_layout,
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confidence_threshold=confidence_threshold
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)
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# Accumulate results
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if page_result['status'] == 'success':
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# Add page number to each text region
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for region in page_result['text_regions']:
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region['page'] = page_num
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all_text_regions.append(region)
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total_confidence_sum += page_result['average_confidence'] * page_result['total_text_regions']
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total_valid_regions += page_result['total_text_regions']
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# Accumulate layout data
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if page_result.get('layout_data'):
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all_layout_data.append(page_result['layout_data'])
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# Accumulate images metadata
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if page_result.get('images_metadata'):
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all_images_metadata.extend(page_result['images_metadata'])
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# Calculate overall average confidence
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avg_confidence = total_confidence_sum / total_valid_regions if total_valid_regions > 0 else 0.0
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# Combine layout data from all pages
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combined_layout = None
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if all_layout_data:
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combined_elements = []
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for layout in all_layout_data:
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if layout.get('elements'):
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combined_elements.extend(layout['elements'])
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if combined_elements:
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combined_layout = {
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'elements': combined_elements,
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'total_elements': len(combined_elements),
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'reading_order': list(range(len(combined_elements))),
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}
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# Generate combined markdown
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markdown_content = self.generate_markdown(all_text_regions, combined_layout)
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# Calculate processing time
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processing_time = (datetime.now() - start_time).total_seconds()
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logger.info(
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f"PDF processing completed: {image_path.name} - "
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f"{len(image_paths)} pages, "
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f"{len(all_text_regions)} regions, "
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f"{avg_confidence:.2f} avg confidence, "
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f"{processing_time:.2f}s"
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)
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return {
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'status': 'success',
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'file_name': image_path.name,
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'language': lang,
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'text_regions': all_text_regions,
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'total_text_regions': len(all_text_regions),
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'average_confidence': avg_confidence,
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'layout_data': combined_layout,
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'images_metadata': all_images_metadata,
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'markdown_content': markdown_content,
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'processing_time': processing_time,
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'timestamp': datetime.utcnow().isoformat(),
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'total_pages': len(image_paths),
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}
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# Get OCR engine (for non-PDF images)
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ocr_engine = self.get_ocr_engine(lang)
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# Perform OCR
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logger.info(f"Processing image: {image_path.name}")
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# Note: In PaddleOCR 3.x, use_angle_cls is set during initialization, not in ocr() call
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ocr_results = ocr_engine.ocr(str(image_path))
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# Parse OCR results (PaddleOCR 3.x format)
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text_regions = []
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total_confidence = 0.0
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valid_regions = 0
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if ocr_results and isinstance(ocr_results, (list, tuple)) and len(ocr_results) > 0:
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# PaddleOCR 3.x returns a list of dictionaries (one per page)
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for page_result in ocr_results:
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if isinstance(page_result, dict):
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# New format: {'rec_texts': [...], 'rec_scores': [...], 'rec_polys': [...]}
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texts = page_result.get('rec_texts', [])
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scores = page_result.get('rec_scores', [])
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polys = page_result.get('rec_polys', [])
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# Process each recognized text
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for idx, text in enumerate(texts):
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# Get corresponding score and bbox
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confidence = scores[idx] if idx < len(scores) else 1.0
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bbox = polys[idx] if idx < len(polys) else []
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# Convert numpy array bbox to list for JSON serialization
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if hasattr(bbox, 'tolist'):
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bbox = bbox.tolist()
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# Filter by confidence threshold
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if confidence >= threshold:
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text_regions.append({
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'text': text,
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'bbox': bbox,
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'confidence': float(confidence),
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})
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total_confidence += confidence
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valid_regions += 1
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avg_confidence = total_confidence / valid_regions if valid_regions > 0 else 0.0
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logger.info(f"Parsed {len(text_regions)} text regions with avg confidence {avg_confidence:.3f}")
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# Layout analysis (if requested)
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layout_data = None
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images_metadata = []
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if detect_layout:
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layout_data, images_metadata = self.analyze_layout(image_path)
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# Generate Markdown
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markdown_content = self.generate_markdown(text_regions, layout_data)
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# Calculate processing time
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processing_time = (datetime.now() - start_time).total_seconds()
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result = {
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'status': 'success',
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'file_name': image_path.name,
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'language': lang,
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'text_regions': text_regions,
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'total_text_regions': len(text_regions),
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'average_confidence': avg_confidence,
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'layout_data': layout_data,
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'images_metadata': images_metadata,
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'markdown_content': markdown_content,
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'processing_time': processing_time,
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'timestamp': datetime.utcnow().isoformat(),
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}
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logger.info(
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f"OCR completed: {image_path.name} - "
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f"{len(text_regions)} regions, "
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f"{avg_confidence:.2f} avg confidence, "
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f"{processing_time:.2f}s"
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)
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return result
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except Exception as e:
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import traceback
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error_trace = traceback.format_exc()
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logger.error(f"OCR processing error for {image_path.name}: {str(e)}\n{error_trace}")
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return {
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'status': 'error',
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'file_name': image_path.name,
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'error_message': str(e),
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'processing_time': (datetime.now() - start_time).total_seconds(),
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}
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def analyze_layout(self, image_path: Path) -> Tuple[Optional[Dict], List[Dict]]:
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"""
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Analyze document layout using PP-StructureV3
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Args:
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image_path: Path to image file
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Returns:
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Tuple of (layout_data, images_metadata)
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"""
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try:
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structure_engine = self.get_structure_engine()
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# Perform structure analysis using predict() method (PaddleOCR 3.x API)
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logger.info(f"Running layout analysis on {image_path.name}")
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results = structure_engine.predict(str(image_path))
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layout_elements = []
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images_metadata = []
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# Process each page result (for images, usually just one page)
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for page_idx, page_result in enumerate(results):
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# Get markdown dictionary from result object
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if hasattr(page_result, 'markdown'):
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markdown_dict = page_result.markdown
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logger.info(f"Page {page_idx} markdown keys: {markdown_dict.keys() if isinstance(markdown_dict, dict) else type(markdown_dict)}")
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# Extract layout information from markdown structure
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if isinstance(markdown_dict, dict):
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# Get markdown texts (HTML format with tables and structure)
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markdown_texts = markdown_dict.get('markdown_texts', '')
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markdown_images = markdown_dict.get('markdown_images', {})
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# Create a layout element for the structured content
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if markdown_texts:
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# Parse HTML content to identify tables and text
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import re
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# Check if content contains tables
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has_table = '<table' in markdown_texts.lower()
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element = {
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'element_id': len(layout_elements),
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'type': 'table' if has_table else 'text',
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'content': markdown_texts,
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'page': page_idx,
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'bbox': [], # PP-StructureV3 doesn't provide individual bbox in this format
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}
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layout_elements.append(element)
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# Add image metadata
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for img_idx, (img_path, img_obj) in enumerate(markdown_images.items()):
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images_metadata.append({
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'element_id': len(layout_elements) + img_idx,
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'image_path': img_path,
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'type': 'image',
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'page': page_idx,
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'bbox': [],
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})
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if layout_elements:
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layout_data = {
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'elements': layout_elements,
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'total_elements': len(layout_elements),
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'reading_order': list(range(len(layout_elements))),
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}
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logger.info(f"Detected {len(layout_elements)} layout elements")
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return layout_data, images_metadata
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else:
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logger.warning("No layout elements detected")
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return None, []
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except Exception as e:
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import traceback
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error_trace = traceback.format_exc()
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logger.error(f"Layout analysis error: {str(e)}\n{error_trace}")
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return None, []
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def generate_markdown(
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self,
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text_regions: List[Dict],
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layout_data: Optional[Dict] = None
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) -> str:
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"""
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Generate Markdown from OCR results
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Args:
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text_regions: List of text regions with bbox and text
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layout_data: Optional layout structure information
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Returns:
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Markdown formatted string
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"""
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markdown_lines = []
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if layout_data and layout_data.get('elements'):
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# Generate structured Markdown based on layout
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for element in layout_data['elements']:
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element_type = element.get('type', 'text')
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content = element.get('content', '')
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if element_type == 'title':
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markdown_lines.append(f"# {content}\n")
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elif element_type == 'table':
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# Table in HTML format
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markdown_lines.append(content)
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markdown_lines.append("")
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elif element_type == 'figure':
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element_id = element.get('element_id')
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markdown_lines.append(f"\n")
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else:
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markdown_lines.append(f"{content}\n")
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else:
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# Simple Markdown from text regions only
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# Sort by vertical position (top to bottom)
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def get_y_coord(region):
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"""Safely extract Y coordinate from bbox"""
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bbox = region.get('bbox', [])
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if isinstance(bbox, (list, tuple)) and len(bbox) > 0:
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if isinstance(bbox[0], (list, tuple)) and len(bbox[0]) > 1:
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return bbox[0][1] # [[x1,y1], [x2,y2], ...] format
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elif len(bbox) > 1:
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return bbox[1] # [x1, y1, x2, y2, ...] format
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return 0 # Default to 0 if can't extract
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sorted_regions = sorted(text_regions, key=get_y_coord)
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for region in sorted_regions:
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text = region['text']
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markdown_lines.append(text)
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return "\n".join(markdown_lines)
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def save_results(
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self,
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result: Dict,
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output_dir: Path,
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file_id: str
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) -> Tuple[Optional[Path], Optional[Path]]:
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"""
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Save OCR results to JSON and Markdown files
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Args:
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result: OCR result dictionary
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output_dir: Output directory
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file_id: Unique file identifier
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Returns:
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Tuple of (json_path, markdown_path)
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"""
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try:
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output_dir.mkdir(parents=True, exist_ok=True)
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# Save JSON
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json_path = output_dir / f"{file_id}_result.json"
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with open(json_path, 'w', encoding='utf-8') as f:
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json.dump(result, f, ensure_ascii=False, indent=2)
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# Save Markdown
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markdown_path = output_dir / f"{file_id}_output.md"
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markdown_content = result.get('markdown_content', '')
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with open(markdown_path, 'w', encoding='utf-8') as f:
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f.write(markdown_content)
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logger.info(f"Results saved: {json_path.name}, {markdown_path.name}")
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return json_path, markdown_path
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except Exception as e:
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logger.error(f"Error saving results: {str(e)}")
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return None, None
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