57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
"""Image OCR endpoint."""
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from fastapi import APIRouter, Depends, HTTPException
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from app.core.dependencies import get_image_processor, get_layout_detector, get_ocr_service, get_mineru_ocr_service
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from app.schemas.image import ImageOCRRequest, ImageOCRResponse
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from app.services.image_processor import ImageProcessor
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from app.services.layout_detector import LayoutDetector
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from app.services.ocr_service import OCRService, MineruOCRService
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router = APIRouter()
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@router.post("/ocr", response_model=ImageOCRResponse)
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async def process_image_ocr(
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request: ImageOCRRequest,
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image_processor: ImageProcessor = Depends(get_image_processor),
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layout_detector: LayoutDetector = Depends(get_layout_detector),
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mineru_service: MineruOCRService = Depends(get_mineru_ocr_service),
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paddle_service: OCRService = Depends(get_ocr_service),
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) -> ImageOCRResponse:
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"""Process an image and extract content as LaTeX, Markdown, and MathML.
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The processing pipeline:
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1. Load and preprocess image (add 30% whitespace padding)
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2. Detect layout using DocLayout-YOLO
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3. Based on layout:
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- If plain text exists: use PP-DocLayoutV2 for mixed recognition
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- Otherwise: use PaddleOCR-VL with formula prompt
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4. Convert output to LaTeX, Markdown, and MathML formats
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Note: OMML conversion is not included due to performance overhead.
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Use the /convert/latex-to-omml endpoint to convert LaTeX to OMML separately.
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"""
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image = image_processor.preprocess(
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image_url=request.image_url,
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image_base64=request.image_base64,
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)
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try:
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if request.model_name == "mineru":
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ocr_result = mineru_service.recognize(image)
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elif request.model_name == "paddle":
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ocr_result = paddle_service.recognize(image)
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else:
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raise HTTPException(status_code=400, detail="Invalid model name")
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except RuntimeError as e:
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raise HTTPException(status_code=503, detail=str(e))
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return ImageOCRResponse(
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latex=ocr_result.get("latex", ""),
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markdown=ocr_result.get("markdown", ""),
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mathml=ocr_result.get("mathml", ""),
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mml=ocr_result.get("mml", ""),
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)
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