---
pipeline_tag: image-text-to-text
language:
- multilingual
tags:
- baidu
- vision-language
- ocr
- custom_code
license: mit
library_name: transformers
---
Unlimited OCR Works
Welcome the Era of One-shot Long-horizon Parsing.
## Release
- [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
```
```python
import os
import torch
from transformers import AutoModel, AutoTokenizer
model_name = 'baidu/Unlimited-OCR'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True,
use_safetensors=True,
torch_dtype=torch.bfloat16,
)
model = model.eval().cuda()
# ── Single image supports two configs: gundam or base ──
# gundam: base_size=1024, image_size=640, crop_mode=True
# base: base_size=1024, image_size=1024, crop_mode=False
model.infer(
tokenizer,
prompt='document parsing.',
image_file='your_image.jpg',
output_path='your/output/dir',
base_size=1024, image_size=640, crop_mode=True,
max_length=32768,
no_repeat_ngram_size=35, ngram_window=128,
save_results=True,
)
# ── Multi page / PDF only uses base (image_size=1024) ──
model.infer_multi(
tokenizer,
prompt='Multi page parsing.',
image_files=['page1.png', 'page2.png', 'page3.png'],
output_path='your/output/dir',
image_size=1024,
max_length=32768,
no_repeat_ngram_size=35, ngram_window=1024,
save_results=True,
)
# ── PDF (convert pages to images, then multi-page parsing) ──
import tempfile, fitz # PyMuPDF
def pdf_to_images(pdf_path, dpi=300):
doc = fitz.open(pdf_path)
tmp_dir = tempfile.mkdtemp(prefix='pdf_ocr_')
mat = fitz.Matrix(dpi / 72, dpi / 72)
paths = []
for i, page in enumerate(doc):
out = os.path.join(tmp_dir, f'page_{i+1:04d}.png')
page.get_pixmap(matrix=mat).save(out)
paths.append(out)
doc.close()
return paths
model.infer_multi(
tokenizer,
prompt='Multi page parsing.',
image_files=pdf_to_images('your_doc.pdf', dpi=300),
output_path='your/output/dir',
image_size=1024,
max_length=32768,
no_repeat_ngram_size=35, ngram_window=1024,
save_results=True,
)
```
### 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:
```python
import base64
import json
import os
import tempfile
import fitz
import requests
from sglang.srt.sampling.custom_logit_processor import DeepseekOCRNoRepeatNGramLogitProcessor
server_url = "http://127.0.0.1:10000"
session = requests.Session()
session.trust_env = False
def pdf_to_images(pdf_path, dpi=300):
doc = fitz.open(pdf_path)
tmp_dir = tempfile.mkdtemp(prefix="pdf_ocr_")
mat = fitz.Matrix(dpi / 72, dpi / 72)
image_paths = []
for i, page in enumerate(doc):
image_path = os.path.join(tmp_dir, f"page_{i + 1:04d}.png")
page.get_pixmap(matrix=mat).save(image_path)
image_paths.append(image_path)
doc.close()
return image_paths
def encode_image(image_path):
ext = os.path.splitext(image_path)[1].lower()
mime = "image/jpeg" if ext in (".jpg", ".jpeg") else f"image/{ext.lstrip('.')}"
with open(image_path, "rb") as f:
data = base64.b64encode(f.read()).decode("utf-8")
return {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{data}"}}
def build_content(prompt, image_paths):
return [{"type": "text", "text": prompt}] + [encode_image(path) for path in image_paths]
def generate(prompt, image_paths, image_mode, ngram_window):
payload = {
"model": "Unlimited-OCR",
"messages": [{"role": "user", "content": build_content(prompt, image_paths)}],
"temperature": 0,
"skip_special_tokens": False,
"images_config": {"image_mode": image_mode},
"custom_logit_processor": DeepseekOCRNoRepeatNGramLogitProcessor.to_str(),
"custom_params": {
"ngram_size": 35,
"window_size": ngram_window,
},
"stream": True,
}
response = session.post(
f"{server_url}/v1/chat/completions",
headers={"Content-Type": "application/json"},
data=json.dumps(payload),
timeout=1200,
stream=True,
)
response.raise_for_status()
chunks = []
for line in response.iter_lines(chunk_size=1, decode_unicode=True):
if not line or not line.startswith("data: "):
continue
data = line[len("data: "):]
if data == "[DONE]":
break
event = json.loads(data)
delta = event["choices"][0].get("delta", {}).get("content", "")
if delta:
print(delta, end="", flush=True)
chunks.append(delta)
print()
return "".join(chunks)
# Single image supports two configs: gundam or base. Example below uses gundam.
generate("document parsing.", ["your_image.jpg"], image_mode="gundam", ngram_window=128)
# Multi image (base only)
generate("Multi page parsing.", ["page1.png", "page2.png"], image_mode="base", ngram_window=1024)
# PDF (base only)
generate("Multi page parsing.", pdf_to_images("your_doc.pdf", dpi=300), image_mode="base", ngram_window=1024)
```
## 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},
}
