DeepSeek-OCR

Product Introduction

1. DeepSeek-OCR is an advanced vision-language model designed to compress long text by treating it as an image, enabling efficient processing of documents through optical compression.
2. The core value of DeepSeek-OCR lies in its ability to drastically reduce the number of vision tokens required to represent lengthy text, optimizing efficiency for long-context AI tasks while delivering robust optical character recognition (OCR) capabilities.

Main Features

1. DeepSeek-OCR employs optical compression to convert text-heavy documents into image-based representations, reducing token consumption by up to 90% compared to traditional text-based processing methods.
2. The model supports multilingual OCR with high accuracy, enabling seamless extraction and conversion of text from images, scanned documents, or PDFs into structured formats like Markdown.
3. It integrates with vLLM for accelerated inference and supports scalable configurations (Tiny, Small, Base, Large, Gundam) with adjustable parameters such as base_size (512–1280) and image_size (512–1280) to balance speed and precision.

Problems Solved

1. DeepSeek-OCR addresses the inefficiency of token-heavy processing in large-language models (LLMs) by compressing text into vision tokens, enabling cost-effective handling of lengthy documents.
2. The model targets developers and enterprises requiring high-throughput OCR solutions for digitizing physical documents, processing PDFs, or analyzing multilingual text in research or business workflows.
3. Typical use cases include converting scanned legal contracts into searchable text, extracting tabular data from financial reports, and automating archival of historical documents in digital libraries.

Unique Advantages

1. Unlike conventional OCR tools, DeepSeek-OCR combines text compression with OCR, allowing downstream AI systems to process compressed visual representations without losing critical textual information.
2. The model leverages flash-attention mechanisms (flash-attn 2.7.3) and a custom vision-language architecture to achieve sub-second inference times on NVIDIA GPUs, even for high-resolution inputs.
3. Competitive advantages include native support for batch processing of PDFs, adaptive cropping (crop_mode=True), and compatibility with Hugging Face transformers, enabling seamless integration into existing AI pipelines.

Frequently Asked Questions (FAQ)

1. How does DeepSeek-OCR handle extremely long documents? The model splits documents into manageable segments using adaptive cropping and processes them iteratively, ensuring consistent accuracy while minimizing GPU memory usage.
2. What file formats are supported for OCR input? DeepSeek-OCR accepts JPEG, PNG, and PDF formats, with PDFs processed page-by-page using integrated vLLM acceleration for multi-page scalability.
3. Is multilingual text extraction supported? Yes, the model supports over 100 languages, including CJK (Chinese-Japanese-Korean) scripts, with specialized tokenizers for handling complex glyph structures.
4. What hardware is required for inference? The model requires NVIDIA GPUs with CUDA 11.8+ and at least 16GB VRAM, optimized for use with torch 2.6.0 and Python 3.12.9 environments.
5. Can the model be fine-tuned for domain-specific documents? Custom fine-tuning is supported via the provided Hugging Face interface, allowing users to adapt the model to specialized layouts like medical charts or engineering blueprints.