Introduction: New Progress in OCR Technology
On January 27, 2026, DeepSeek AI released DeepSeek-OCR-2, an end-to-end OCR system based on the DeepEncoder V2 architecture. The model achieved 91.09% accuracy on OmniDocBench v1.5, a 3.73% improvement over its predecessor.
The core feature of DeepSeek-OCR-2 is its human-like reading order for document processing, rather than traditional raster scanning. This design enables better performance when handling multi-column documents, tables, and complex layouts. The model is fully open-source under the Apache-2.0 license and can be used in commercial projects.
This article provides a detailed introduction to DeepSeek-OCR-2's technical architecture, performance data, hardware requirements, and practical application scenarios.
What is DeepSeek-OCR-2?
DeepSeek-OCR-2 is a vision-language OCR model designed to extract text from images. The model uses an end-to-end architecture, eliminating the need for traditional OCR's multi-stage processing pipeline (detection, recognition, post-processing).
Basic Parameters
Total Parameters: 3B (3 billion), with approximately 570M activated parameters
Vision Encoder: 380M parameters (SAM-base 80M + Qwen2-0.5B 300M)
Language Decoder: DeepSeek-3B-MoE (64 experts, 6 activated per inference)
Visual Token Range: 256-1120 tokens
Open Source License: Apache-2.0
Release Date: January 27, 2026
Differences from Traditional OCR
Traditional OCR systems typically consist of three independent modules:
Text detection (locating text regions)
Text recognition (identifying characters)
Post-processing (error correction, formatting)
DeepSeek-OCR-2 adopts an end-to-end design, directly generating text output from images. This approach reduces error accumulation between modules and improves overall accuracy.
Open Source and Availability
GitHub: https://github.com/deepseek-ai/DeepSeek-OCR-2
HuggingFace: https://huggingface.co/deepseek-ai/DeepSeek-OCR-2
Paper: arXiv:2601.20552
License: Apache-2.0 (commercial use allowed)
DeepEncoder V2: Core Technical Architecture
DeepEncoder V2 is the core innovation of DeepSeek-OCR-2, addressing problems in traditional vision-language models for document understanding.
Limitations of Traditional VLMs
Traditional vision-language models use a fixed raster scanning order (top-left to bottom-right), which has the following issues:
Cannot understand document structure: Multi-column documents, tables, and other complex layouts are processed incorrectly
Unnatural reading order: Does not align with human reading habits
Loss of semantic information: Cannot adjust processing order based on content importance
For example, when processing a two-column document, traditional models read in the order "top-left → top-right → bottom-left → bottom-right," while the correct order should be "top-left → bottom-left → top-right → bottom-right."
Dual-Stream Attention Mechanism
DeepEncoder V2 adopts a dual-stream attention design:
Visual tokens: Use bidirectional attention to maintain global receptive field
Causal flow queries: Use causal attention (similar to LLM decoders), only attending to previous tokens
This design allows the model to first establish global understanding, then decide the reading order.
Semantic Reordering
DeepEncoder V2 dynamically reorders visual information through learnable query vectors:
Vision encoder extracts image features
Causal flow queries reorder features based on semantic importance
Language model generates output based on reordered sequence
This process simulates how humans read documents: first browse globally, identify important regions, then read in logical order.
Cascaded Causal Reasoning
DeepSeek-OCR-2 employs two-stage causal reasoning:
Stage 1: Vision encoder performs preliminary causal reasoning, generating reordered visual sequence
Stage 2: Language model generates text output based on reordered sequence
This cascaded design improves the model's ability to understand complex documents.
Performance Benchmarks: Evaluation Data Analysis
OmniDocBench v1.5 Evaluation Results
DeepSeek-OCR-2 achieved the following results on OmniDocBench v1.5:
Overall Score: 91.09% (SOTA end-to-end model)
Reading Order Edit Distance: 0.057 (33% reduction from v1's 0.085)
Complex Layout Accuracy: Excellent
Table Recognition Accuracy: Excellent
Mathematical Formula Recognition: Excellent
Comparison with Mainstream Models
| Model | Visual Tokens | Overall Score | Reading Order | Complex Layout | Tables | Math Formulas |
|---|
| DeepSeek-OCR-2 | 256-1120 | 91.09% | ✅ Human-like | Excellent | Excellent | Excellent |
| DeepSeek-OCR-1 | 256-1120 | 87.36% | ❌ Raster | Good | Good | Good |
| Gemini-3 Pro | ~1120 | 87.5% | ❌ Raster | Good | Good | Very Good |
| GOT-OCR2.0 | 256 | 85.2% | ❌ Raster | Good | Very Good | Good |
*Data sources: TechNode report, Proxnox benchmarks*
The comparison data shows that DeepSeek-OCR-2 significantly leads in overall score and reading order. Particularly in complex layout processing, the human-like reading order brings significant advantages.
Hardware Requirements and Deployment
Inference Hardware Requirements
Minimum Configuration:
GPU: NVIDIA RTX 3090 (24GB VRAM)
RAM: 32GB
Storage: 50GB available space
Recommended Configuration:
GPU: NVIDIA A100 (40GB VRAM)
RAM: 64GB
Storage: 100GB available space
Production Environment:
GPU: Multi-card cluster (8× A100 or more)
RAM: 256GB+
Storage: 1TB+ SSD
Processing Throughput
Single GPU (A100-40G): ~200,000 pages/day
Cluster (20 nodes × 8 A100): ~33 million pages/day
Practical Applications
Document Digitization: Historical archives, library collections
Form Recognition: Invoices, contracts, medical records
Multilingual Recognition: 100+ languages support
Complex Layout Processing: Academic papers, technical manuals
Handwriting Recognition: Handwritten notes, signatures
Real-time OCR: Mobile applications
FAQ
Q: Which languages are supported?
A: 100+ languages including Chinese, English, Japanese, Korean, etc.
Q: Can it be deployed offline?
A: Yes, fully offline deployment is supported.
Q: Is commercial use free?
A: Yes, Apache-2.0 license allows free commercial use.
Q: How to get started?
A: Visit GitHub or HuggingFace to download the model and follow the documentation.
Summary
DeepSeek-OCR-2 achieves human-like reading order through the DeepEncoder V2 architecture, scoring 91.09% on OmniDocBench v1.5. The model excels in complex layouts, multilingual recognition, and table processing.
Link
Z-Image: Free AI Image Generator
Z-Image-Turbo: Free AI Image Generator
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Zimage.run Google Site
Zhi Hu
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Keywords: DeepSeek-OCR-2, OCR model, text recognition, optical character recognition, deep learning OCR, end-to-end OCR, vision-language model, open-source OCR