Unlimited OCR

---
pipeline_tag: image-text-to-text
language:

• multilingual

• baidu

• vision-language

• ocr

• custom_code

<hr>

<h1 align="center">Unlimited OCR Works</h1>

<div align="center">

<a href="https://trendshift.io/repositories/62053?utm_source=trendshift-badge&amp;utm_medium=badge&amp;utm_campaign=badge-trendshift-62053" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/62053/daily" alt="baidu%2FUnlimited-OCR | Trendshift" width="250" height="55"/></a>

<a href="https://github.com/baidu/Unlimited-OCR">
<img alt="GitHub" src="https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white" />
</a>
<a href="https://huggingface.co/baidu/Unlimited-OCR">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-ffc107?color=ffc107&logoColor=white" />
</a>
</div>

<div align="center">
<a href="https://arxiv.org/abs/2606.23050">
<img alt="arXiv" src="https://img.shields.io/badge/arXiv-Unlimited OCR Works-b31b1b?logo=arxiv&logoColor=white" />
</a>
<a href="https://x.com/Baidu_Inc" target="_blank">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Baidu Inc.-white?logo=x&logoColor=white" />
</a>
</div>

<h3 align="center">Welcome the Era of One-shot Long-horizon Parsing.</h3>

<p align="center">
<img src="assets/Unlimited-OCR.png" width="1000" alt="Unlimited OCR overview" />
</p>

Release

• [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift.

• [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud.

• [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference.

• [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces.

• [2026/06/23] 📄 Our paper is now available on arXiv.

• [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope.

• [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further.

Inference

Transformers

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

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='<image>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='<image>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='<image>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

##### Docker Images
Use the following Docker images depending on your GPU platform:

Default (CUDA 13.0):

docker pull vllm/vllm-openai:unlimited-ocr

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:

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:

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,
},
"s