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.
90%+
Reduce manual data entry workload
30+
Common medical document types covered
50+
Core healthcare fields extracted accurately
95%+
Information 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.
Document Classification
Receipt Extraction
Medical Record Extraction
Card & Certificate Extraction
Key strengths
Key strengths
Deep medical semantic understanding
It goes beyond text recognition to understand medical terminology, diagnostic phrasing, and business logic, adapting to different writing habits across hospitals.
Stable in complex real-world scenarios
It maintains high recognition and extraction accuracy across messy handwriting, stamp occlusion, folded captures, and mixed multi-page medical documents.
Reliable results ready for production
Extracted fields are automatically normalized with pixel-level traceability, reducing manual review and enabling direct integration into business systems.
Full-process batch handling
It supports continuous parsing and batch extraction for multi-page documents, greatly improving efficiency for medical archiving and insurance settlement workflows.
Low-threshold fast integration
It supports mainstream formats such as images and PDFs, with standardized API integration that fits existing medical systems without complex development.
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 casesStructured 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
CapabilitiesAutomatically identifies different medical document types for batch classification and archiving.
Accurately reconstructs complex medical layouts and nested table structures.
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.
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.
Flexible pricing, tailored solutions, and private deployment
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.
Reduce manual data entry workload
Common medical document types covered
Core healthcare fields extracted accurately
Information 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.
Document Classification
Receipt Extraction
Medical Record Extraction
Card & Certificate Extraction
Key strengths
Deep medical semantic understanding
It goes beyond text recognition to understand medical terminology, diagnostic phrasing, and business logic, adapting to different writing habits across hospitals.
Stable in complex real-world scenarios
It maintains high recognition and extraction accuracy across messy handwriting, stamp occlusion, folded captures, and mixed multi-page medical documents.
Reliable results ready for production
Extracted fields are automatically normalized with pixel-level traceability, reducing manual review and enabling direct integration into business systems.
Full-process batch handling
It supports continuous parsing and batch extraction for multi-page documents, greatly improving efficiency for medical archiving and insurance settlement workflows.
Low-threshold fast integration
It supports mainstream formats such as images and PDFs, with standardized API integration that fits existing medical systems without complex development.
Technical highlights
Deep 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
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
- Automatically identifies different medical document types for batch classification and archiving.
- Accurately reconstructs complex medical layouts and nested table structures.
- 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.
- 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.
