first
This commit is contained in:
beabigegg
516
backend/app/services/ocr_service.py
Normal file
516
backend/app/services/ocr_service.py
Normal file
@@ -0,0 +1,516 @@
|
||||
"""
|
||||
Tool_OCR - Core OCR Service
|
||||
PaddleOCR-VL integration for text and structure extraction
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
from datetime import datetime
|
||||
import uuid
|
||||
|
||||
from paddleocr import PaddleOCR, PPStructureV3
|
||||
from PIL import Image
|
||||
from pdf2image import convert_from_path
|
||||
|
||||
from app.core.config import settings
|
||||
from app.services.office_converter import OfficeConverter, OfficeConverterError
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class OCRService:
|
||||
"""
|
||||
Core OCR service using PaddleOCR-VL
|
||||
Handles text recognition and document structure analysis
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize PaddleOCR and PPStructure engines"""
|
||||
self.ocr_languages = settings.ocr_languages_list
|
||||
self.confidence_threshold = settings.ocr_confidence_threshold
|
||||
|
||||
# Initialize PaddleOCR engine (will be lazy-loaded per language)
|
||||
self.ocr_engines = {}
|
||||
|
||||
# Initialize PP-Structure for layout analysis
|
||||
self.structure_engine = None
|
||||
|
||||
# Initialize Office document converter
|
||||
self.office_converter = OfficeConverter()
|
||||
|
||||
logger.info("OCR Service initialized")
|
||||
|
||||
def get_ocr_engine(self, lang: str = 'ch') -> PaddleOCR:
|
||||
"""
|
||||
Get or create OCR engine for specified language
|
||||
|
||||
Args:
|
||||
lang: Language code (ch, en, japan, korean, etc.)
|
||||
|
||||
Returns:
|
||||
PaddleOCR engine instance
|
||||
"""
|
||||
if lang not in self.ocr_engines:
|
||||
logger.info(f"Initializing PaddleOCR engine for language: {lang}")
|
||||
self.ocr_engines[lang] = PaddleOCR(
|
||||
use_angle_cls=True,
|
||||
lang=lang,
|
||||
# Note: show_log and use_gpu parameters removed in PaddleOCR 3.x
|
||||
)
|
||||
logger.info(f"PaddleOCR engine ready for {lang}")
|
||||
|
||||
return self.ocr_engines[lang]
|
||||
|
||||
def get_structure_engine(self) -> PPStructureV3:
|
||||
"""
|
||||
Get or create PP-Structure engine for layout analysis
|
||||
|
||||
Returns:
|
||||
PPStructure engine instance
|
||||
"""
|
||||
if self.structure_engine is None:
|
||||
logger.info("Initializing PP-StructureV3 engine")
|
||||
self.structure_engine = PPStructureV3(
|
||||
use_doc_orientation_classify=False,
|
||||
use_doc_unwarping=False,
|
||||
use_textline_orientation=False,
|
||||
use_table_recognition=True,
|
||||
use_formula_recognition=True,
|
||||
layout_threshold=0.5,
|
||||
)
|
||||
logger.info("PP-StructureV3 engine ready")
|
||||
|
||||
return self.structure_engine
|
||||
|
||||
def convert_pdf_to_images(self, pdf_path: Path, output_dir: Path) -> List[Path]:
|
||||
"""
|
||||
Convert PDF to images (one per page)
|
||||
|
||||
Args:
|
||||
pdf_path: Path to PDF file
|
||||
output_dir: Directory to save converted images
|
||||
|
||||
Returns:
|
||||
List of paths to converted images
|
||||
"""
|
||||
try:
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
logger.info(f"Converting PDF {pdf_path.name} to images")
|
||||
|
||||
# Convert PDF to images (300 DPI for good quality)
|
||||
images = convert_from_path(
|
||||
str(pdf_path),
|
||||
dpi=300,
|
||||
fmt='png'
|
||||
)
|
||||
|
||||
image_paths = []
|
||||
for i, image in enumerate(images):
|
||||
# Save each page as PNG
|
||||
image_path = output_dir / f"{pdf_path.stem}_page_{i+1}.png"
|
||||
image.save(str(image_path), 'PNG')
|
||||
image_paths.append(image_path)
|
||||
logger.info(f"Saved page {i+1} to {image_path.name}")
|
||||
|
||||
logger.info(f"Converted {len(image_paths)} pages from PDF")
|
||||
return image_paths
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"PDF conversion error: {str(e)}")
|
||||
raise
|
||||
|
||||
def process_image(
|
||||
self,
|
||||
image_path: Path,
|
||||
lang: str = 'ch',
|
||||
detect_layout: bool = True,
|
||||
confidence_threshold: Optional[float] = None
|
||||
) -> Dict:
|
||||
"""
|
||||
Process single image with OCR and layout analysis
|
||||
|
||||
Args:
|
||||
image_path: Path to image file
|
||||
lang: Language for OCR
|
||||
detect_layout: Whether to perform layout analysis
|
||||
confidence_threshold: Minimum confidence threshold (uses default if None)
|
||||
|
||||
Returns:
|
||||
Dictionary with OCR results and metadata
|
||||
"""
|
||||
start_time = datetime.now()
|
||||
threshold = confidence_threshold if confidence_threshold is not None else self.confidence_threshold
|
||||
|
||||
try:
|
||||
# Check if file is Office document
|
||||
if self.office_converter.is_office_document(image_path):
|
||||
logger.info(f"Detected Office document: {image_path.name}, converting to PDF")
|
||||
try:
|
||||
# Convert Office document to PDF
|
||||
pdf_path = self.office_converter.convert_to_pdf(image_path)
|
||||
logger.info(f"Office document converted to PDF: {pdf_path.name}")
|
||||
|
||||
# Process the PDF (will be handled by PDF processing logic below)
|
||||
image_path = pdf_path
|
||||
except OfficeConverterError as e:
|
||||
logger.error(f"Office conversion failed: {str(e)}")
|
||||
raise
|
||||
|
||||
# Check if file is PDF
|
||||
is_pdf = image_path.suffix.lower() == '.pdf'
|
||||
|
||||
if is_pdf:
|
||||
# Convert PDF to images
|
||||
logger.info(f"Detected PDF file: {image_path.name}, converting to images")
|
||||
pdf_images_dir = image_path.parent / f"{image_path.stem}_pages"
|
||||
image_paths = self.convert_pdf_to_images(image_path, pdf_images_dir)
|
||||
|
||||
# Process all pages
|
||||
all_text_regions = []
|
||||
total_confidence_sum = 0.0
|
||||
total_valid_regions = 0
|
||||
all_layout_data = []
|
||||
all_images_metadata = []
|
||||
|
||||
for page_num, page_image_path in enumerate(image_paths, 1):
|
||||
logger.info(f"Processing PDF page {page_num}/{len(image_paths)}")
|
||||
|
||||
# Process each page
|
||||
page_result = self.process_image(
|
||||
page_image_path,
|
||||
lang=lang,
|
||||
detect_layout=detect_layout,
|
||||
confidence_threshold=confidence_threshold
|
||||
)
|
||||
|
||||
# Accumulate results
|
||||
if page_result['status'] == 'success':
|
||||
# Add page number to each text region
|
||||
for region in page_result['text_regions']:
|
||||
region['page'] = page_num
|
||||
all_text_regions.append(region)
|
||||
|
||||
total_confidence_sum += page_result['average_confidence'] * page_result['total_text_regions']
|
||||
total_valid_regions += page_result['total_text_regions']
|
||||
|
||||
# Accumulate layout data
|
||||
if page_result.get('layout_data'):
|
||||
all_layout_data.append(page_result['layout_data'])
|
||||
|
||||
# Accumulate images metadata
|
||||
if page_result.get('images_metadata'):
|
||||
all_images_metadata.extend(page_result['images_metadata'])
|
||||
|
||||
# Calculate overall average confidence
|
||||
avg_confidence = total_confidence_sum / total_valid_regions if total_valid_regions > 0 else 0.0
|
||||
|
||||
# Combine layout data from all pages
|
||||
combined_layout = None
|
||||
if all_layout_data:
|
||||
combined_elements = []
|
||||
for layout in all_layout_data:
|
||||
if layout.get('elements'):
|
||||
combined_elements.extend(layout['elements'])
|
||||
if combined_elements:
|
||||
combined_layout = {
|
||||
'elements': combined_elements,
|
||||
'total_elements': len(combined_elements),
|
||||
'reading_order': list(range(len(combined_elements))),
|
||||
}
|
||||
|
||||
# Generate combined markdown
|
||||
markdown_content = self.generate_markdown(all_text_regions, combined_layout)
|
||||
|
||||
# Calculate processing time
|
||||
processing_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
logger.info(
|
||||
f"PDF processing completed: {image_path.name} - "
|
||||
f"{len(image_paths)} pages, "
|
||||
f"{len(all_text_regions)} regions, "
|
||||
f"{avg_confidence:.2f} avg confidence, "
|
||||
f"{processing_time:.2f}s"
|
||||
)
|
||||
|
||||
return {
|
||||
'status': 'success',
|
||||
'file_name': image_path.name,
|
||||
'language': lang,
|
||||
'text_regions': all_text_regions,
|
||||
'total_text_regions': len(all_text_regions),
|
||||
'average_confidence': avg_confidence,
|
||||
'layout_data': combined_layout,
|
||||
'images_metadata': all_images_metadata,
|
||||
'markdown_content': markdown_content,
|
||||
'processing_time': processing_time,
|
||||
'timestamp': datetime.utcnow().isoformat(),
|
||||
'total_pages': len(image_paths),
|
||||
}
|
||||
|
||||
# Get OCR engine (for non-PDF images)
|
||||
ocr_engine = self.get_ocr_engine(lang)
|
||||
|
||||
# Perform OCR
|
||||
logger.info(f"Processing image: {image_path.name}")
|
||||
# Note: In PaddleOCR 3.x, use_angle_cls is set during initialization, not in ocr() call
|
||||
ocr_results = ocr_engine.ocr(str(image_path))
|
||||
|
||||
# Parse OCR results (PaddleOCR 3.x format)
|
||||
text_regions = []
|
||||
total_confidence = 0.0
|
||||
valid_regions = 0
|
||||
|
||||
if ocr_results and isinstance(ocr_results, (list, tuple)) and len(ocr_results) > 0:
|
||||
# PaddleOCR 3.x returns a list of dictionaries (one per page)
|
||||
for page_result in ocr_results:
|
||||
if isinstance(page_result, dict):
|
||||
# New format: {'rec_texts': [...], 'rec_scores': [...], 'rec_polys': [...]}
|
||||
texts = page_result.get('rec_texts', [])
|
||||
scores = page_result.get('rec_scores', [])
|
||||
polys = page_result.get('rec_polys', [])
|
||||
|
||||
# Process each recognized text
|
||||
for idx, text in enumerate(texts):
|
||||
# Get corresponding score and bbox
|
||||
confidence = scores[idx] if idx < len(scores) else 1.0
|
||||
bbox = polys[idx] if idx < len(polys) else []
|
||||
|
||||
# Convert numpy array bbox to list for JSON serialization
|
||||
if hasattr(bbox, 'tolist'):
|
||||
bbox = bbox.tolist()
|
||||
|
||||
# Filter by confidence threshold
|
||||
if confidence >= threshold:
|
||||
text_regions.append({
|
||||
'text': text,
|
||||
'bbox': bbox,
|
||||
'confidence': float(confidence),
|
||||
})
|
||||
total_confidence += confidence
|
||||
valid_regions += 1
|
||||
|
||||
avg_confidence = total_confidence / valid_regions if valid_regions > 0 else 0.0
|
||||
|
||||
logger.info(f"Parsed {len(text_regions)} text regions with avg confidence {avg_confidence:.3f}")
|
||||
|
||||
# Layout analysis (if requested)
|
||||
layout_data = None
|
||||
images_metadata = []
|
||||
|
||||
if detect_layout:
|
||||
layout_data, images_metadata = self.analyze_layout(image_path)
|
||||
|
||||
# Generate Markdown
|
||||
markdown_content = self.generate_markdown(text_regions, layout_data)
|
||||
|
||||
# Calculate processing time
|
||||
processing_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
result = {
|
||||
'status': 'success',
|
||||
'file_name': image_path.name,
|
||||
'language': lang,
|
||||
'text_regions': text_regions,
|
||||
'total_text_regions': len(text_regions),
|
||||
'average_confidence': avg_confidence,
|
||||
'layout_data': layout_data,
|
||||
'images_metadata': images_metadata,
|
||||
'markdown_content': markdown_content,
|
||||
'processing_time': processing_time,
|
||||
'timestamp': datetime.utcnow().isoformat(),
|
||||
}
|
||||
|
||||
logger.info(
|
||||
f"OCR completed: {image_path.name} - "
|
||||
f"{len(text_regions)} regions, "
|
||||
f"{avg_confidence:.2f} avg confidence, "
|
||||
f"{processing_time:.2f}s"
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
error_trace = traceback.format_exc()
|
||||
logger.error(f"OCR processing error for {image_path.name}: {str(e)}\n{error_trace}")
|
||||
return {
|
||||
'status': 'error',
|
||||
'file_name': image_path.name,
|
||||
'error_message': str(e),
|
||||
'processing_time': (datetime.now() - start_time).total_seconds(),
|
||||
}
|
||||
|
||||
def analyze_layout(self, image_path: Path) -> Tuple[Optional[Dict], List[Dict]]:
|
||||
"""
|
||||
Analyze document layout using PP-StructureV3
|
||||
|
||||
Args:
|
||||
image_path: Path to image file
|
||||
|
||||
Returns:
|
||||
Tuple of (layout_data, images_metadata)
|
||||
"""
|
||||
try:
|
||||
structure_engine = self.get_structure_engine()
|
||||
|
||||
# Perform structure analysis using predict() method (PaddleOCR 3.x API)
|
||||
logger.info(f"Running layout analysis on {image_path.name}")
|
||||
results = structure_engine.predict(str(image_path))
|
||||
|
||||
layout_elements = []
|
||||
images_metadata = []
|
||||
|
||||
# Process each page result (for images, usually just one page)
|
||||
for page_idx, page_result in enumerate(results):
|
||||
# Get markdown dictionary from result object
|
||||
if hasattr(page_result, 'markdown'):
|
||||
markdown_dict = page_result.markdown
|
||||
logger.info(f"Page {page_idx} markdown keys: {markdown_dict.keys() if isinstance(markdown_dict, dict) else type(markdown_dict)}")
|
||||
|
||||
# Extract layout information from markdown structure
|
||||
if isinstance(markdown_dict, dict):
|
||||
# Get markdown texts (HTML format with tables and structure)
|
||||
markdown_texts = markdown_dict.get('markdown_texts', '')
|
||||
markdown_images = markdown_dict.get('markdown_images', {})
|
||||
|
||||
# Create a layout element for the structured content
|
||||
if markdown_texts:
|
||||
# Parse HTML content to identify tables and text
|
||||
import re
|
||||
|
||||
# Check if content contains tables
|
||||
has_table = '<table' in markdown_texts.lower()
|
||||
|
||||
element = {
|
||||
'element_id': len(layout_elements),
|
||||
'type': 'table' if has_table else 'text',
|
||||
'content': markdown_texts,
|
||||
'page': page_idx,
|
||||
'bbox': [], # PP-StructureV3 doesn't provide individual bbox in this format
|
||||
}
|
||||
layout_elements.append(element)
|
||||
|
||||
# Add image metadata
|
||||
for img_idx, (img_path, img_obj) in enumerate(markdown_images.items()):
|
||||
images_metadata.append({
|
||||
'element_id': len(layout_elements) + img_idx,
|
||||
'image_path': img_path,
|
||||
'type': 'image',
|
||||
'page': page_idx,
|
||||
'bbox': [],
|
||||
})
|
||||
|
||||
if layout_elements:
|
||||
layout_data = {
|
||||
'elements': layout_elements,
|
||||
'total_elements': len(layout_elements),
|
||||
'reading_order': list(range(len(layout_elements))),
|
||||
}
|
||||
logger.info(f"Detected {len(layout_elements)} layout elements")
|
||||
return layout_data, images_metadata
|
||||
else:
|
||||
logger.warning("No layout elements detected")
|
||||
return None, []
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
error_trace = traceback.format_exc()
|
||||
logger.error(f"Layout analysis error: {str(e)}\n{error_trace}")
|
||||
return None, []
|
||||
|
||||
def generate_markdown(
|
||||
self,
|
||||
text_regions: List[Dict],
|
||||
layout_data: Optional[Dict] = None
|
||||
) -> str:
|
||||
"""
|
||||
Generate Markdown from OCR results
|
||||
|
||||
Args:
|
||||
text_regions: List of text regions with bbox and text
|
||||
layout_data: Optional layout structure information
|
||||
|
||||
Returns:
|
||||
Markdown formatted string
|
||||
"""
|
||||
markdown_lines = []
|
||||
|
||||
if layout_data and layout_data.get('elements'):
|
||||
# Generate structured Markdown based on layout
|
||||
for element in layout_data['elements']:
|
||||
element_type = element.get('type', 'text')
|
||||
content = element.get('content', '')
|
||||
|
||||
if element_type == 'title':
|
||||
markdown_lines.append(f"# {content}\n")
|
||||
elif element_type == 'table':
|
||||
# Table in HTML format
|
||||
markdown_lines.append(content)
|
||||
markdown_lines.append("")
|
||||
elif element_type == 'figure':
|
||||
element_id = element.get('element_id')
|
||||
markdown_lines.append(f"\n")
|
||||
else:
|
||||
markdown_lines.append(f"{content}\n")
|
||||
|
||||
else:
|
||||
# Simple Markdown from text regions only
|
||||
# Sort by vertical position (top to bottom)
|
||||
def get_y_coord(region):
|
||||
"""Safely extract Y coordinate from bbox"""
|
||||
bbox = region.get('bbox', [])
|
||||
if isinstance(bbox, (list, tuple)) and len(bbox) > 0:
|
||||
if isinstance(bbox[0], (list, tuple)) and len(bbox[0]) > 1:
|
||||
return bbox[0][1] # [[x1,y1], [x2,y2], ...] format
|
||||
elif len(bbox) > 1:
|
||||
return bbox[1] # [x1, y1, x2, y2, ...] format
|
||||
return 0 # Default to 0 if can't extract
|
||||
|
||||
sorted_regions = sorted(text_regions, key=get_y_coord)
|
||||
|
||||
for region in sorted_regions:
|
||||
text = region['text']
|
||||
markdown_lines.append(text)
|
||||
|
||||
return "\n".join(markdown_lines)
|
||||
|
||||
def save_results(
|
||||
self,
|
||||
result: Dict,
|
||||
output_dir: Path,
|
||||
file_id: str
|
||||
) -> Tuple[Optional[Path], Optional[Path]]:
|
||||
"""
|
||||
Save OCR results to JSON and Markdown files
|
||||
|
||||
Args:
|
||||
result: OCR result dictionary
|
||||
output_dir: Output directory
|
||||
file_id: Unique file identifier
|
||||
|
||||
Returns:
|
||||
Tuple of (json_path, markdown_path)
|
||||
"""
|
||||
try:
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Save JSON
|
||||
json_path = output_dir / f"{file_id}_result.json"
|
||||
with open(json_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(result, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# Save Markdown
|
||||
markdown_path = output_dir / f"{file_id}_output.md"
|
||||
markdown_content = result.get('markdown_content', '')
|
||||
with open(markdown_path, 'w', encoding='utf-8') as f:
|
||||
f.write(markdown_content)
|
||||
|
||||
logger.info(f"Results saved: {json_path.name}, {markdown_path.name}")
|
||||
return json_path, markdown_path
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving results: {str(e)}")
|
||||
return None, None
|
||||
Reference in New Issue
Block a user