Baidu Releases Unlimited-OCR, a 3B Parameter Document Parsing Model Based on Deepseek-OCR

Baidu has released Unlimited-OCR, a 3 billion parameter optical character recognition model designed for document parsing. Released on June 22, 2025, the model builds on Deepseek-OCR and is available on Hugging Face.

Technical Specifications

Unlimited-OCR operates with a 32,768 token context window and uses bfloat16 precision on NVIDIA GPUs. The model requires PyTorch 2.10.0, transformers 4.57.1, and CUDA 12.9. According to Baidu, the model is positioned as "pushing Deepseek-OCR one step further" with support for "one-shot long-horizon parsing."

The model offers two processing modes:

• Gundam mode: 1024 base size, 640 image size with crop mode enabled for single images
• Base mode: 1024 base and image size without cropping for single images and all multi-page documents

Deployment Options

Unlimited-OCR can be deployed via Hugging Face transformers or SGLang server infrastructure. The SGLang deployment requires FlashAttention 3 backend and supports an OpenAI-compatible API with streaming responses.

For multi-page documents and PDFs, the model converts pages to images at 300 DPI before processing. The implementation includes custom logit processors with a 35-token n-gram constraint and configurable window sizes (128 tokens for single images, 1,024 tokens for multi-page documents).

Technical Implementation

The model uses PyMuPDF for PDF-to-image conversion and supports both single-image and multi-page inference. Base64-encoded images are sent to the model with text prompts like "document parsing" or "Multi page parsing." The SGLang server configuration allocates 80% of GPU memory statically and disables overlap scheduling.

Baidu acknowledges Deepseek-OCR, Deepseek-OCR-2, and PaddleOCR in the model documentation. Pricing information has not been disclosed.

What This Means

Unlimited-OCR adds another option to the OCR model landscape, though it remains unclear how performance compares to existing solutions like GPT-4V or Claude 3.5 Sonnet on document understanding tasks. The 3B parameter size suggests efficient inference, but no benchmark scores have been published. The model's value proposition depends on comparative accuracy data that Baidu has not yet provided.