Context Optical Compression Engine

deepseek ocr

DeepSeek OCR compresses high-resolution documents into lean vision tokens, then decodes them with a 3B-parameter mixture-of-experts model to deliver near-lossless text, layout, and diagram understanding across 100+ languages.

Precision

97%

Exact-match accuracy on Fox benchmark at 10× compression.

Throughput

200k

Pages per day on a single NVIDIA A100 GPU using DeepSeek OCR.

Languages

100+

Multilingual DeepSeek OCR coverage across diverse scripts.

Explore DeepSeek OCR
Vision Tokens Compression Ratio Active MoE Params

Tiny → Base → Large → Gundam progression visualizes how DeepSeek OCR maintains low token counts while scaling visual fidelity.

What is DeepSeek OCR?

DeepSeek OCR is a two-stage transformer-based document AI that compresses page images into compact vision tokens before decoding them with a high-capacity mixture-of-experts language model. Stage 1 merges a windowed SAM vision transformer with a dense CLIP-Large encoder and a 16× convolutional compressor; Stage 2 uses the DeepSeek-3B-MoE decoder (~570M active parameters per token) to reconstruct text, HTML, and figure annotations with minimal loss.

Trained on 30 million real PDF pages plus synthetic charts, formulas, and diagrams, DeepSeek OCR preserves layout structure, tables, chemistry (SMILES strings), and geometry tasks. Its CLIP heritage maintains multimodal competence—captions and object grounding remain intact even after aggressive compression.

DeepSeek OCR Context Optical Compression

By reducing a 1024×1024 page to just 256 tokens, DeepSeek OCR enables long-document ingestion that would overwhelm conventional OCR pipelines, keeping global semantics while slashing compute requirements.

DeepSeek OCR Multilingual Reach

More than 100 languages—including Latin, CJK, Cyrillic, and specialized scientific scripts—benefit from DeepSeek OCR’s training distribution, enabling global digitization and data generation projects.

Feature Stack of DeepSeek OCR

DeepSeek OCR Vision Encoder

80M-parameter windowed SAM plus 300M-parameter CLIP-Large align local glyph detail with global layout features, retaining fidelity in dense legal, financial, and scientific PDFs.

DeepSeek OCR Mode Selector

From Tiny (64 tokens) to Gundam (multi-viewport tiling), DeepSeek OCR allows precision tuning between speed and fidelity for invoices, blueprints, and large-format scans.

DeepSeek OCR Structured Output

Outputs HTML tables, Markdown charts, SMILES chemistry, and geometry annotations, enabling direct ingestion into analytics pipelines without manual reconstruction.

DeepSeek OCR Compliance Considerations

MIT-licensed weights let organizations run DeepSeek OCR on-premises, avoiding regulatory scrutiny tied to DeepSeek’s Chinese infrastructure when using hosted APIs.

DeepSeek OCR Architecture Deep Dive

Stage 1 · DeepSeek OCR DeepEncoder (~380M)

Rasterized pages (up to 1280×1280) split into 4096 patches, compressed 16× into 256–400 tokens. Local windows ensure glyph accuracy while CLIP-Large preserves page semantics.

Stage 2 · DeepSeek OCR MoE Decoder (3B)

The mixture-of-experts decoder activates ~570M parameters per token, reconstructing text, layout tags, and captions. FlashAttention and CUDA optimizations sustain GPU throughput.

DeepSeek OCR Multimodal Bridge

CLIP pretraining lets DeepSeek OCR align textual summaries with diagrams, charts, and figures—vital for scientific documents and data visualization handoffs.

DeepSeek OCR Data Pathway

Compression to decoding pipeline keeps context intact:

1. High-resolution PDF page (640–1280 px)

SAM patch extraction

2. 16× convolutional compression to 64–400 tokens

Context optical compression

3. DeepSeek OCR MoE decoding (~570M active)

FlashAttention acceleration

4. Output structured HTML, Markdown, or captions

Layout-preserving results

DeepSeek OCR Benchmark Comparisons

Benchmark studies indicate DeepSeek OCR delivers state-of-the-art accuracy on structured documents while maintaining low token budgets.

OCR System Accuracy Snapshot Speed / Throughput Core Strengths Deployment
DeepSeek OCR ~97% exact match at 10× compression ~200k pages/day per NVIDIA A100 Layout-rich OCR, tables, formulas, diagrams, multilingual Open-source (MIT); Local GPU or DeepSeek API
Google Cloud Vision ~98% on mixed benchmarks Elastic cloud throughput Enterprise support, multilingual APIs Proprietary pay-per-use API
AWS Textract ~97–99% on forms Managed cloud scaling Invoice & form extraction with JSON output Proprietary pay-per-use API
Azure OCR ~99.8% on clean typed text Azure ecosystem integrations Strong for printed pages; handwriting variance Proprietary pay-per-use API
Tesseract OSS ~90–95% depending on scans Local CPU/GPU Open-source, handwriting friendly Open-source (Apache 2.0)

Sources: Fox compression benchmark, OmniDocBench, AI Multiple accuracy reviews, DeepSeek documentation.

How to Use DeepSeek OCR

01

Deploy DeepSeek OCR locally with GPUs

Clone the DeepSeek OCR GitHub repo, download the 6.7 GB safetensors checkpoint, and configure PyTorch 2.6+ with FlashAttention. Base mode runs on 8–10 GB GPUs, while Gundam tiling benefits from 40 GB A100s.

02

Call DeepSeek OCR via API

Utilize DeepSeek’s OpenAI-compatible API endpoints to submit images and receive structured text. Pricing mirrors the platform’s token billing (~$0.028 per million input tokens for cache hits).

03

Integrate DeepSeek OCR into workflows

Convert OCR outputs to JSON, link SMILES strings to cheminformatics pipelines, or auto-caption diagrams for bilingual publishing—all using DeepSeek OCR’s structured results.

DeepSeek OCR Operational Guardrails

  • Schedule latency-sensitive jobs on Base or Large modes; queue archival batches in Tiny mode to stretch GPU hours.
  • Pair DeepSeek OCR with retrieval-augmented generation pipelines to summarize lengthy documents while respecting layout context.
  • Review regional compliance when leveraging DeepSeek’s hosted API; local deployments avoid cross-border data exposure.
  • Combine with handwriting-focused engines (e.g., Tesseract) when cursive accuracy is a requirement.

DeepSeek OCR Use Cases

DeepSeek OCR for Scanned Books & Reports

Compress thousands of words per page into compact tokens for downstream search, summarization, and knowledge graph pipelines.

DeepSeek OCR for Technical Diagrams & Formulas

Extract geometry reasoning, engineering annotations, and chemical SMILES from visual assets to support scientific analysis.

DeepSeek OCR Multilingual Dataset Creation

Build global corpora across 100+ languages, scanning books or surveys to create training data for downstream language models.

DeepSeek OCR Document Conversion Apps

Embed into invoice, contract, or form-processing platforms to emit layout-aware JSON and HTML ready for automation.

DeepSeek OCR Limitations & Mitigations

DeepSeek OCR Compression Trade-offs

Accuracy drops to ~60% at 20× compression; opt for Large or Gundam modes when microtext or dense tables are present.

DeepSeek OCR Vector Graphic Challenges

Fine vector charts remain tough; combine with vector-native parsers when CAD precision is essential.

DeepSeek OCR Handwriting Gaps

Primarily trained on printed text; supplement with handwriting OCR tools for cursive-heavy workloads.

DeepSeek OCR GPU Dependency

Real-time throughput requires modern GPUs. Batch processing or DeepSeek’s managed API can smooth compute needs.

DeepSeek OCR Licensing & Pricing

DeepSeek OCR MIT Open-source Freedom

Download the ~6.7 GB safetensors checkpoint and operate DeepSeek OCR locally without license fees, customizing workflows to your compliance standards.

DeepSeek OCR API Token Economics

Hosted access follows DeepSeek’s token pricing (~$0.028 per million input tokens for cache hits). Plan budgets around compression mode and document volume.

Hardware planning: a single A100 (~200k pages/day) can drive enterprise queues, while 20 nodes × 8 A100s reach ~33 million pages/day for large-scale digitization.

DeepSeek OCR FAQ

How does DeepSeek OCR compress long documents?

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.

Which GPUs power DeepSeek OCR effectively?

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.

Does DeepSeek OCR handle handwriting?

Handwriting is not a core focus; performance remains limited compared to specialized cursive OCR tools. Pair DeepSeek OCR with handwriting engines when needed.

Can DeepSeek OCR preserve tables and charts?

Yes. Tests show near-lossless HTML/Markdown reproduction for tables and chart structures, enabling analytics pipelines without manual clean-up.

How multilingual is DeepSeek OCR?

DeepSeek OCR covers roughly 100 languages, spanning Latin, CJK, Cyrillic, and scientific notation, thanks to its extensive real and synthetic training data.

What output formats can DeepSeek OCR produce?

DeepSeek OCR can emit plain text, HTML, Markdown, structured JSON, SMILES chemistry strings, and contextual captions, depending on prompts.

Is DeepSeek OCR safe for regulated industries?

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.

How does DeepSeek OCR compare with cloud OCR services?

It matches or exceeds cloud competitors on complex documents while using far fewer vision tokens, making it ideal for GPU-constrained operations.

What tooling ecosystem supports DeepSeek OCR?

Hugging Face Spaces, community notebooks, and “awesome DeepSeek” repositories showcase demos, while SDKs integrate with Adobe, Figma, and Python clients.

Can DeepSeek OCR assist with context archiving?

Yes. Store conversations as images to expand LLM context windows, and let DeepSeek OCR reconstruct the text when required.

DeepSeek OCR Voices from X

Practitioners and researchers across the globe are sharing how DeepSeek OCR’s context optical compression shifts their document workflows. Explore a curated feed of reactions captured from X (Twitter).

DeepSeek OCR Research Paper

Dive deeper into the context optical compression paradigm, architecture, and benchmarks by downloading the official PDF. Review it offline to explore detailed experiments, ablations, and deployment guidance straight from the DeepSeek OCR team.

Download DeepSeek OCR Paper PDF · 6 MB · MIT

Accelerate document intelligence with DeepSeek OCR

Digitize, analyze, and restructure complex PDFs, charts, and multilingual archives using context optical compression.

Access DeepSeek OCR Repository Read Technical Brief