Deepseek-ocr
What is DeepSeek OCR[2]?
DeepSeek OCR is an advanced optical character recognition[1] (OCR) tool that leverages a two-stage transformer-based architecture[3] to compress and decode high-resolution document images into structured text, layouts, and annotations. It utilizes a context optical compression[4] system that transforms complex page layouts into compact vision tokens. The first stage employs a combination of a windowed SAM vision transformer and a dense CLIP-Large encoder, while the second stage utilizes a mixture-of-experts (MoE) decoder with 3 billion parameters to reconstruct the original document information with near-lossless accuracy. This powerful tool supports over 100 languages, making it ideal for global document digitization projects.
How to use DeepSeek OCR?
- Deploy DeepSeek OCR locally with GPUs: Clone the DeepSeek OCR GitHub repository, download the 6.7 GB safetensors checkpoint, and set up PyTorch 2.6+ with FlashAttention. Ensure your GPU has at least 8–10 GB of VRAM for Base mode, while Gundam mode requires 40 GB A100s.
- Call DeepSeek OCR via API: Use DeepSeek’s OpenAI-compatible API endpoints to submit images and receive structured text outputs. Pricing is based on token usage, approximately $0.028 per million input tokens for cache hits.
- Integrate DeepSeek OCR into workflows: Convert the OCR outputs to formats like JSON, link SMILES strings to cheminformatics pipelines, or auto-generate captions for diagrams, utilizing the structured results from DeepSeek OCR.
What are the main features of DeepSeek OCR?
- Context Optical Compression: Reduces high-resolution documents into compact vision tokens, enabling efficient processing of complex layouts.
- Multilingual Support[5]: Capable of processing over 100 languages, including Latin, CJK, and specialized scientific scripts.
- Structured Output[6]: Outputs in various formats such as HTML, Markdown, and JSON, facilitating easy integration into analytics workflows.
- High Throughput: Achieves up to 200,000 pages per day on a single NVIDIA A100 GPU, making it suitable for large-scale document processing.
- Compliance Considerations: MIT-licensed weights allow for local deployment, minimizing regulatory concerns associated with cloud-based solutions.
Who is DeepSeek OCR for?
DeepSeek OCR is designed for organizations and professionals involved in document digitization, data extraction, and multilingual processing. It is particularly beneficial for industries such as legal, financial, and scientific sectors that require accurate and efficient handling of complex documents. Researchers, data scientists, and developers looking to integrate advanced OCR capabilities into their applications or workflows will find DeepSeek OCR to be a valuable tool.
What are the use cases of DeepSeek OCR?
- Scanned Books & Reports: Efficiently compress thousands of words per page for search and summarization in digital libraries.
- Technical Diagrams & Formulas: Extract detailed geometry reasoning and chemical annotations from visual assets to support scientific analysis.
- Multilingual Dataset Creation: Build diverse training datasets across 100+ languages by scanning books or surveys for language model development.
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Deepseek-ocr Pros and Cons
Pros
- High Compression Efficiency: DeepSeek OCR achieves a remarkable 10× compression ratio, allowing for efficient processing of high-resolution documents while maintaining near-lossless text and layout understanding.
- Multilingual Support: With support for over 100 languages, including Latin, CJK, and Cyrillic scripts, DeepSeek OCR is suitable for global digitization projects.
- GPU Optimization: Designed for GPU efficiency, DeepSeek OCR can process up to 200,000 pages per day on a single NVIDIA A100 GPU, making it ideal for high-volume document processing.
Cons
No cons data detected for this tool
Deepseek-ocr Pricing
DeepSeek Reasoner
Pricing for DeepSeek Reasoner model.
Input Tokens (Cache Miss)
Pricing for input tokens when cache miss occurs.
Output Tokens
Pricing for output tokens.
For the latest pricing, please visit this link: https://api-docs.deepseek.com/quick_start/pricing
Prices are subject to change. Please visit the official website for the most up-to-date pricing information.
Analytics of Deepseek-ocr
Deepseek-ocr Website Traffic Analysis
Visits Over Time
Traffic Sources
Nov 2025 - Dec 2025 Worldwide Desktop Only
- Search: 72.38%
- Direct: 18.12%
- Referrals: 7.01%
- Social: 1.50%
- Paid Referrals: 0.55%
- Mail: 0.17%
Top Keywords
| Keyword | Volume | CPC | Estimated Value |
|---|---|---|---|
| deepseek ocr 坐标 | 0 | $0.00 | $120.00 |
| deepseek ocr | 46.59K | $2.07 | $2130.00 |
| deepseek-ocr | 8.82K | $0.00 | $340.00 |
| deepseak ocr | 200 | $0.00 | $80.00 |
| deeps ocr | 90 | $0.00 | $80.00 |
Deepseek-ocr Reviews
DeepSeek OCR! Open source is a gift that keeps on giving! AWESOME! I just converted a 400 page PDF into markdown using this fine new open source model. It took under 4 minutes!
Unlike closed AI labs, DeepSeek proves they are truly open research. Their OCR paper treats paragraphs as pixels and is 60x leap more efficient than traditional LLMs. Small super efficient models are the future.
The big blue whale is back with something wild this time! DeepSeek built an OCR model that can compress text by 10x using vision tokens.
For more reviews, visit this link: https://deepseek-ocr.io#voices-from-x
Deepseek-ocr Compare
| Tool Name | Introduction | Pricing | Type | Rating | Launch Date | Learn more |
|---|---|---|---|---|---|---|
Explore AI Lawyer for easy, quick, and budget-friendly legal help. Empowering consumers and lawyers with AI-driven solutions for all your legal needs. | Free | 💼Work | February 11, 2023 | Get deal | ||
2Mn+ readymade ChatGPT prompt ideas built by prompt engineers, using insights from eCommerce experts - that really work! | Free | 💼Work🎨Creativity | February 6, 2023 | Get deal | ||
Rewind | Free | 🙋♂️Personal💼Work | February 2, 2018 | Get deal |
Info current as of post date. Offers and availability may vary by location and are subject to change.
Deepseek-ocr Q&A
DeepSeek OCR slices pages into patches, applies 16× convolutional downsampling, and forwards only 64–400 vision tokens to the MoE decoder, retaining layout cues while cutting context size tenfold.
For more FAQs, please visit this link: https://deepseek-ocr.io/#faq
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