# CheatSheet: baidu/Unlimited-OCR
## Source: Standard Context (/main/README.md)
Unlimited OCR Works
Welcome the Era of One-shot Long-horizon Parsing.
## Release
- [2026/07/21] 🤝 Thanks to the [ms-swift community](https://github.com/modelscope/ms-swift) for their support, our model now supports training with [ms-swift](https://github.com/modelscope/ms-swift).
- [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on [Baidu Cloud](https://cloud.baidu.com/doc/OCR/s/fmr1p39gb).
- [2026/06/28] 🤝 Thanks to the [vLLM community](https://github.com/vllm-project/vllm) and [Tianyu Guo](https://github.com/gty111) for their support, our model now supports vLLM inference.
- [2026/06/24] 🤝 Thanks to [AK](https://x.com/_akhaliq) for creating a demo for us. It is now available at [Hugging Face Spaces](https://huggingface.co/spaces/baidu/Unlimited-OCR).
- [2026/06/23] 📄 Our paper is now available on [arXiv](https://arxiv.org/abs/2606.23050).
- [2026/06/23] 🤝 Thanks to the [ModelScope community](https://github.com/modelscope) for their support. Our model is now available at [ModelScope](https://modelscope.cn/models/PaddlePaddle/Unlimited-OCR).
- [2026/06/22] 🚀 We present [Unlimited-OCR](https://github.com/baidu/Unlimited-OCR), aiming to push [Deepseek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR) one step further.
## Inference
### Transformers
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.3 + CUDA12.9：
```
torch==2.10.0
torchvision==0.25.0
transformers==4.57.1
Pillow==12.1.1
matplotlib==3.10.8
einops==0.8.2
addict==2.4.0
easydict==1.13
pymupdf==1.27.2.2
psutil==7.2.2
```
```
/* Detailed source-code truncated for AI context efficiency. */
```
### vLLM
Please refer to the official vLLM recipe for deployment details:
**Recipe:** [https://recipes.vllm.ai/baidu/Unlimited-OCR](https://recipes.vllm.ai/baidu/Unlimited-OCR)
##### Docker Images
Use the following Docker images depending on your GPU platform:
**Default (CUDA 13.0):**
```bash
docker pull vllm/vllm-openai:unlimited-ocr
```
**For Hopper GPUs (CUDA 12.9)**
```bash
docker pull vllm/vllm-openai:unlimited-ocr-cu129
```
### SGLang
Set up the environment (uv-managed virtualenv). Install the local SGLang wheel first,
then pin `kernels==0.9.0` and install PyMuPDF for PDF-to-image conversion:
```shell
uv venv --python 3.12
source .venv/bin/activate
uv pip install wheel/sglang-0.0.0.dev11416+g92e8bb79e-py3-none-any.whl
uv pip install kernels==0.11.7
uv pip install pymupdf==1.27.2.2
```
Start the SGLang server:
```shell
python -m sglang.launch_server \
--model baidu/Unlimited-OCR \
--served-model-name Unlimited-OCR \
--attention-backend fa3 \
--page-size 1 \
--mem-fraction-static 0.8 \
--context-length 32768 \
--enable-custom-logit-processor \
--disable-overlap-schedule \
--skip-server-warmup \
--host 0.0.0.0 \
--port 10000
```
Send streaming requests to the OpenAI-compatible API:
```
/* Detailed source-code truncated for AI context efficiency. */
```
For batch inference, `infer.py` starts the SGLang server automatically and sends concurrent requests for an image directory or PDF:
```shell
# Image directory
python infer.py \
--image_dir ./examples/images \
--output_dir ./outputs \
--concurrency 8 \
--image_mode gundam
# PDF pages
python infer.py \
--pdf ./examples/document.pdf \
--output_dir ./outputs \
--concurrency 8 \
--image_mode gundam
```
Useful options:
```shell
--model_dir baidu/Unlimited-OCR # Local path or Hugging Face model ID
--gpu 0 # CUDA_VISIBLE_DEVICES value
--server_log ./log/sglang_server.log
```
For OmniDocBench evaluation, you need to perform the following post-processing.
```python
DET_RE = re.compile(r'<\|det\|>([^<\s]+)(?:\s*\[[^\]]*\])?\s*<\|/det\|>(.*)', re.DOTALL)
def remove_det(raw: str) -> str:
"""
Strip <|det|>type [bbox]<|/det|> markers, group lines belonging to the
same block with \\n, and separate different blocks with \\n\\n.
"""
blocks = []
cur = None
for line in raw.splitlines():
line = line.rstrip()
if not line:
continue
m = DET_RE.match(line)
if m:
category, content = m.group(1).strip(), m.group(2).strip()
if category == 'image':
continue
if cur is not None:
blocks.append(cur)
cur = [content] if content else []
continue
if cur is None:
cur = []
cur.append(line)
if cur is not None:
blocks.append(cur)
text = '\n\n'.join('\n'.join(b) for b in blocks).strip()
return text
```
## Visualization
## Acknowledgement
We would like to thank [Deepseek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR), [Deepseek-OCR-2](https://github.com/deepseek-ai/DeepSeek-OCR-2), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) for their valuable models and ideas.
## Citation
```bibtex
@misc{yin2026unlimitedocrworks,
title={Unlimited OCR Works},
author={Youyang Yin and Huanhuan Liu and YY and Qunyi Xie and Chaorun Liu and Shiqi Yang and Shaohua Wang and Zhanlong Liu and Hao Zou and Jinyue Chen and Shu Wei and Jingjing Wu and Mingxin Huang and Zhen Wu and Guibin Wang and Tengyu Du and Lei Jia},
year={2026},
eprint={2606.23050},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.23050},
}
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