U1-OCR-Med
Medical docs, smart layouts, precise extraction
Medical document parsing for classification, archiving, and extraction
U1-OCR-Med: Medical docs, smart layouts, precise extraction
U1-OCR-MED is a document intelligence model purpose-built for healthcare scenarios, providing one-stop medical document classification and professional information extraction. It is precisely adapted to medical records, examination reports, prescriptions, billing documents, and other healthcare paperwork, efficiently handling common industry pain points such as messy handwriting, terminology abbreviations, stamp occlusion, and complex layouts. It also supports zero-shot cross-domain generalization, balancing medical-grade accuracy with deployment efficiency.
Manual Entry Workload Saved
Common Document Types Covered
Core Medical Fields Extracted
Extraction Accuracy
U1-OCR-MED shows leading performance across medical document classification and multi-scenario information extraction tasks. Medical document classification accuracy reaches 98.2%, with overall recognition capability significantly outperforming mainstream peer models such as Gemini and Qwen. Receipt extraction accuracy reaches 95.31% and medical record extraction accuracy reaches 95.65%. Even with professional medical terminology and varied writing styles, it maintains high precision at an industry-leading level. Card and certificate extraction accuracy reaches 98.87%, providing strong scenario adaptability and stable recognition for high-precision, high-reliability medical deployments.
Medical Document Intelligence
Document Classification
Invoice Extraction
Medical Record Extraction
ID & Certificate Extraction
Key strengths
Key strengths
Deep medical semantic understanding
Goes beyond text recognition to understand medical terminology, diagnostic statements and business logic, adapting to different hospitals' writing styles.
Stable across complex scenarios
Maintains high recognition and extraction precision for messy handwriting, seal occlusion, creased photos and mixed multi-page layouts.
Trustworthy, production-ready results
Extracted fields are auto-standardized with pixel-level traceability, requiring minimal manual review before feeding directly into business systems.
Full-pipeline batch processing
Supports coherent multi-page parsing and batch extraction, greatly improving efficiency for medical archiving and insurance settlement.
Low-barrier, fast integration
Compatible with mainstream image and PDF formats, with standardized API integration requiring minimal development to fit into existing medical systems.
Technical highlights
Technical highlightsDeep fusion of medical knowledge and multimodality
It deeply combines medical knowledge bases with vision-language alignment, going beyond OCR text reading to truly understand medical terms, diagnostic meaning, and business logic.
OCR 3.0 deep semantic understanding architecture
It continues third-generation document semantic understanding, overcoming the shallow recognition limits of traditional CRNNs and the layout weaknesses of ordinary VLMs.
Adaptive parsing for complex medical layouts
It is natively adapted to non-standard layouts such as medical records, prescriptions, and examination reports, supporting mixed documents and automatic splitting with independent parsing.
Highly robust recognition for irregular scenarios
Purpose-built for real healthcare pain points such as messy handwriting, medical abbreviations and terminology, seal occlusion, skewed photos, and incomplete content, delivering strong stability across long-tail cases.
Use Cases
Use Cases
Structured archiving of medical records
Automatic classification and information extraction for inpatient and discharge records, outpatient records, and progress notes.
Intelligent parsing of examination reports
Structured processing and indicator extraction for imaging, lab, and pathology reports.
Processing medical billing documents
Amount detail extraction and reconciliation for charging lists and settlement receipts.
Support for medical insurance and commercial insurance
Structured parsing of claim documents, intelligent classification of reimbursement materials, and information validation.
Capabilities
Capabilities-
Automatically identifies different medical document types for batch classification and archiving.
Accurately reconstructs complex medical layouts and nested table structures.
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Extracts core business fields such as patient data, diagnoses, test indicators, medication details, and fee breakdowns.
Adapts to formatting differences across hospitals and standardizes field mapping.
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Supports continuous parsing and batch processing of multi-page documents to improve workflow efficiency.
Handles handwritten content, stamp occlusion, terminology abbreviations, and other complex healthcare scenarios.
- Adapts to format differences across hospitals for standardized field mapping.
- Supports coherent multi-page parsing and batch processing to greatly improve efficiency.
- Compatible with handwriting, seal occlusion, abbreviated terminology and other complex medical scenarios.
Get StartedFlexible pricing, custom solutions, private deployment
Flexible billing models and dedicated customization for medical document parsing scenarios, with private deployment to ensure data security and compliance
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