# Unlimited-OCR **Repository Path**: devai/Unlimited-OCR ## Basic Information - **Project Name**: Unlimited-OCR - **Description**: 长文档超长上下文 OCR 模型，主打一次性长文档解析、超长文本生成，提出 “单次长跨度文档解析” 方案。解决传统 OCR 多页 PDF / 长文档分段推理丢失上下文、重复文本、记忆衰减问题，支持最大 32768 超长上下文窗口。 - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-06-23 - **Last Updated**: 2026-06-23 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Baidu Inc.

Unlimited OCR Works

GitHub Hugging Face
arXiv Twitter Follow

Welcome the Era of One-shot Long-horizon Parsing.

Unlimited OCR overview

## Release - [2026/06/22] 🚀 We present [Unlimited-OCR](https://github.com/baidu/Unlimited-OCR), aiming to push [Deepseek-OCR](https://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, ) ``` ### 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) ``` 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 ``` ## Visualization Long-horizon OCR demo ## Acknowledgement We would like to thank [Deepseek-OCR](https://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 Coming soon!