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OCR Service (Python/FastAPI):
- POST /extract for synchronous OCR extraction
- POST /jobs and GET /jobs/{job_id} for async processing
- Image preprocessing (deskew, denoise) for accuracy
- HEIC conversion via pillow-heif
- Redis job queue for async processing
Backend (Fastify):
- POST /api/ocr/extract - authenticated proxy to OCR
- POST /api/ocr/jobs - async job submission
- GET /api/ocr/jobs/:jobId - job polling
- Multipart file upload handling
- JWT authentication required
File size limits: 10MB sync, 200MB async
Processing time target: <3 seconds for typical photos
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
19 lines
309 B
Python
19 lines
309 B
Python
"""Pydantic models for OCR service."""
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from .schemas import (
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DocumentType,
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ExtractedField,
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JobResponse,
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JobStatus,
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JobSubmitRequest,
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OcrResponse,
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)
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__all__ = [
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"DocumentType",
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"ExtractedField",
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"JobResponse",
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"JobStatus",
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"JobSubmitRequest",
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"OcrResponse",
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]
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