DeepSeek OCR
is a high-accuracy optical character recognition model built for extracting text from complex visual inputs such as documents, screenshots, receipts, and natural scenes.
It combines vision-language modeling with efficient visual encoders to achieve superior recognition of multi-language and multi-layout text while remaining lightweight enough for edge or on-device deployment.
Features
Multilingual OCR
— recognizes printed and handwritten text across major global languages.
Document Layout Understanding
— preserves structure such as tables, paragraphs, and titles.
Scene Text Recognition
— robust against lighting, distortion, and low-quality captures.
Lightweight & Fast
— optimized for CPU and GPU acceleration.
End-to-End Pipeline
— supports image-to-text and structured JSON output.
Use Cases
Digitizing scanned documents or PDFs
Extracting text from mobile camera inputs or screenshots
Invoice and receipt parsing
OCR-based search and indexing systems
Visual question answering or document agents
Inputs and Outputs
Input:
Image file (JPEG, PNG, or tensor array)
Optional parameters for language hints or layout detection
Output:
Extracted text (plain text or structured format with bounding boxes)
Confidence scores per word or region
Integration
DeepSeek OCR can be integrated through:
Python API (
pip install deepseek-ocr
)
REST or gRPC endpoints for server deployment
License
This model is released under the
Apache 2.0 License
, allowing commercial use, modification, and redistribution with attribution.
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