Q.How does DeepSeek OCR compress long documents?
A.DeepSeek OCR slices pages into patches, applies 16x convolutional downsampling, and forwards only 64–400 vision tokens to the MoE decoder, retaining layout cues while cutting context size tenfold.
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Deepseek OCR
💲Paid
DeepSeek OCR is an advanced document AI system that uses transformer-based technology and context optical compression to achieve high accuracy in text, layout, and diagram understanding across multiple languages. It efficiently processes complex layouts while preserving structure and supports integration with existing workflows.
💻
Platform
web
OCRDocument AIDocument IntelligenceContext Optical CompressionMultilingual OCRStructured OutputTransformer
What is Deepseek OCR?
DeepSeek OCR is an advanced document AI system that leverages transformer-based technology to provide high-quality optical character recognition (OCR). It employs a two-stage process involving context optical compression to transform high-resolution documents into efficient vision tokens, which are then decoded using a sophisticated 3B-parameter mixture-of-experts model. This system excels in near-lossless text, layout, and diagram understanding across over 100 languages. Trained on a vast dataset of 30 million real PDF pages and synthetic data, it maintains accuracy in complex layouts, tables, chemical notations (SMILES strings), and geometric tasks while ensuring GPU-efficient performance.
Core Technologies
- Transformer-based AI
- Optical Character Recognition (OCR)
- Mixture-of-Experts Model
Key Capabilities
- Near-lossless text extraction across 100+ languages
- Accurate layout and diagram understanding
- Efficient handling of complex document structures
- Support for tables, chemical notations, and geometry tasks
- GPU-efficient processing for high-throughput applications
- Trained on extensive real and synthetic PDF datasets
Use Cases
- Document digitization and archiving
- Multilingual text extraction and translation
- Automated data entry from forms and invoices
- Academic research and paper analysis
- Legal document processing and analysis
- Chemical and geometric diagram recognition
Core Benefits
- Near-lossless text, layout, and diagram understanding
- High accuracy across diverse document types
- Efficient processing of complex layouts
- Preserves layout structure, tables, and specialized content
- Scalable for large document volumes
- Easy integration with existing workflows
Key Features
- Two-stage transformer-based document AI system
- Context optical compression for lean vision tokens
- 3B-parameter mixture-of-experts model for decoding
- Supports over 100 languages
- GPU-efficient throughput for complex layouts
- Trained on 30 million real PDF pages plus synthetic data
How to Use
- 1Clone the GitHub repository and download the 6.7 GB checkpoint file
- 2Install required dependencies including PyTorch and configure your GPU environment
- 3Submit images via the OpenAI-compatible API to receive structured text outputs
- 4Convert OCR results to JSON or integrate with existing workflows as needed
Pricing Plans
API Input Tokens (Cache Hit)
$0.028
Per 1M input tokens when cache is hit
API Input Tokens (Cache Miss)
$0.28
Per 1M input tokens when cache is missed
API Output Tokens
$0.42
Per 1M output tokens
Frequently Asked Questions
Q.Which GPUs power DeepSeek OCR effectively?
A.NVIDIA A100 (40 GB) offers peak throughput (~200k pages/day), while RTX 30-series cards with ≥8 GB VRAM can handle Base mode for moderate loads.
Q.Does DeepSeek OCR handle handwriting?
A.Handwriting is not a core focus; performance remains limited compared to specialized cursive OCR tools. It's recommended to pair DeepSeek OCR with handwriting engines when needed.
Q.Can DeepSeek OCR preserve tables and charts?
A.Yes. Tests show near-lossless HTML/Markdown reproduction for tables and chart structures, enabling analytics pipelines without manual clean-up.
Q.Is DeepSeek OCR safe for regulated industries?
A.Local deployment keeps data on-prem under the MIT license. When using DeepSeek’s API, consult compliance guidance due to scrutiny of the company’s cloud infrastructure.
Pros & Cons (Reserved)
✓ Pros
- High accuracy in text and layout understanding
- Supports over 100 languages for global use
- Efficient GPU processing for complex documents
- Preserves tables and specialized content like chemistry
- Scalable for handling large volumes of documents
✗ Cons
- Requires significant GPU resources for optimal performance
- Complex setup may deter non-technical users
- Limited customization options for specific use cases
Alternatives
No alternatives provided.