--- license: other language: - multilingual pipeline_tag: image-text-to-text library_name: transformers base_model: - tencent/HunyuanOCR tags: - ocr - hunyuan - vision-language - image-to-text - 1B - end-to-end ---

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HunyuanOCR

## 📖 Introduction **HunyuanOCR** stands as a leading end-to-end OCR expert VLM powered by Hunyuan's native multimodal architecture. With a remarkably lightweight 1B parameter design, it has achieved multiple state-of-the-art benchmarks across the industry. The model demonstrates mastery in **complex multilingual document parsing** while excelling in practical applications including **text spotting, open-field information extraction, video subtitle extraction, and photo translation**. ## 🚀 Quick Start with Transformers ### Installation ```bash pip install git+https://github.com/huggingface/transformers@82a06db03535c49aa987719ed0746a76093b1ec4 ``` > **Note**: We will merge it into the Transformers main branch later. ### Model Inference ```python from transformers import AutoProcessor from transformers import HunYuanVLForConditionalGeneration from PIL import Image import torch def clean_repeated_substrings(text): """Clean repeated substrings in text""" n = len(text) if n<8000: return text for length in range(2, n // 10 + 1): candidate = text[-length:] count = 0 i = n - length while i >= 0 and text[i:i + length] == candidate: count += 1 i -= length if count >= 10: return text[:n - length * (count - 1)] return text model_name_or_path = "tencent/HunyuanOCR" processor = AutoProcessor.from_pretrained(model_name_or_path, use_fast=False) img_path = "path/to/your/image.jpg" image_inputs = Image.open(img_path) messages1 = [ {"role": "system", "content": ""}, { "role": "user", "content": [ {"type": "image", "image": img_path}, {"type": "text", "text": ( "检测并识别图片中的文字，将文本坐标格式化输出。" )}, ], } ] messages = [messages1] texts = [ processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages ] inputs = processor( text=texts, images=image_inputs, padding=True, return_tensors="pt", ) model = HunYuanVLForConditionalGeneration.from_pretrained( model_name_or_path, attn_implementation="eager", dtype=torch.bfloat16, device_map="auto" ) with torch.no_grad(): device = next(model.parameters()).device inputs = inputs.to(device) generated_ids = model.generate(**inputs, max_new_tokens=16384, do_sample=False) if "input_ids" in inputs: input_ids = inputs.input_ids else: print("inputs: # fallback", inputs) input_ids = inputs.inputs generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(input_ids, generated_ids) ] output_texts = clean_repeated_substrings(processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False )) print(output_texts) ``` ## 🚀 Quick Start with vLLM Checkout [vLLM HunyuanOCR Usage Guide](https://docs.vllm.ai/projects/recipes/en/latest/Tencent-Hunyuan/HunyuanOCR.html). ### Installation ```bash uv venv hunyuanocr source hunyuanocr/bin/activate uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` Note: We suggest to install [cuda-compat-12-9](https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/): ```bash sudo dpkg -i cuda-compat-12-9_575.57.08-0ubuntu1_amd64.deb echo 'export LD_LIBRARY_PATH=/usr/local/cuda-12.9/compat:$LD_LIBRARY_PATH' >> ~/.bashrc source ~/.bashrc # verify cuda-compat-12-9 ls /usr/local/cuda-12.9/compat ``` ### Model Deploy ```bash vllm serve tencent/HunyuanOCR \ --no-enable-prefix-caching \ --mm-processor-cache-gb 0 \ --gpu-memory-utilization 0.2 ``` ### Model Inference ```python from vllm import LLM, SamplingParams from PIL import Image from transformers import AutoProcessor def clean_repeated_substrings(text): """Clean repeated substrings in text""" n = len(text) if n<8000: return text for length in range(2, n // 10 + 1): candidate = text[-length:] count = 0 i = n - length while i >= 0 and text[i:i + length] == candidate: count += 1 i -= length if count >= 10: return text[:n - length * (count - 1)] return text model_path = "tencent/HunyuanOCR" llm = LLM(model=model_path, trust_remote_code=True) processor = AutoProcessor.from_pretrained(model_path) sampling_params = SamplingParams(temperature=0, max_tokens=16384) img_path = "/path/to/image.jpg" img = Image.open(img_path) messages = [ {"role": "system", "content": ""}, {"role": "user", "content": [ {"type": "image", "image": img_path}, {"type": "text", "text": "检测并识别图片中的文字，将文本坐标格式化输出。"} ]} ] prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = {"prompt": prompt, "multi_modal_data": {"image": [img]}} output = llm.generate([inputs], sampling_params)[0] print(clean_repeated_substrings(output.outputs[0].text)) ``` ## 💬 Application-oriented Prompts | Task | English | Chinese | |------|---------|---------| | **Spotting** | Detect and recognize text in the image, and output the text coordinates in a formatted manner. | 检测并识别图片中的文字，将文本坐标格式化输出。 | | **Parsing** | • Identify the formula in the image and represent it using LaTeX format.

• Parse the table in the image into HTML.

• Parse the chart in the image; use Mermaid format for flowcharts and Markdown for other charts.

• Extract all information from the main body of the document image and represent it in markdown format, ignoring headers and footers. Tables should be expressed in HTML format, formulas in the document should be represented using LaTeX format, and the parsing should be organized according to the reading order. | • 识别图片中的公式，用 LaTeX 格式表示。

• 把图中的表格解析为 HTML。

• 解析图中的图表，对于流程图使用 Mermaid 格式表示，其他图表使用 Markdown 格式表示。

• 提取文档图片中正文的所有信息用 markdown 格式表示，其中页眉、页脚部分忽略，表格用 html 格式表达，文档中公式用 latex 格式表示，按照阅读顺序组织进行解析。 | | **Information Extraction** | • Output the value of Key.

• Extract the content of the fields: ['key1','key2', ...] from the image and return it in JSON format.

• Extract the subtitles from the image. | • 输出 Key 的值。

• 提取图片中的: ['key1','key2', ...] 的字段内容，并按照 JSON 格式返回。

• 提取图片中的字幕。 | | **Translation** | First extract the text, then translate the text content into English. If it is a document, ignore the header and footer. Formulas should be represented in LaTeX format, and tables should be represented in HTML format. | 先提取文字，再将文字内容翻译为英文。若是文档，则其中页眉、页脚忽略。公式用latex格式表示，表格用html格式表示。 | ## 🤝 Join Our Community

| Wechat Discussion Group | Discord Group | | :---: | :---: | |

| [Join HunyuanOCR Discord](https://discord.gg/XeD3p2MRDk) |

## 📚 Citation ``` @misc{hunyuanvisionteam2025hunyuanocrtechnicalreport, title={HunyuanOCR Technical Report}, author={Hunyuan Vision Team and Pengyuan Lyu and Xingyu Wan and Gengluo Li and Shangpin Peng and Weinong Wang and Liang Wu and Huawen Shen and Yu Zhou and Canhui Tang and Qi Yang and Qiming Peng and Bin Luo and Hower Yang and Xinsong Zhang and Jinnian Zhang and Houwen Peng and Hongming Yang and Senhao Xie and Longsha Zhou and Ge Pei and Binghong Wu and Kan Wu and Jieneng Yang and Bochao Wang and Kai Liu and Jianchen Zhu and Jie Jiang and Linus and Han Hu and Chengquan Zhang}, year={2025}, journal={arXiv preprint arXiv:2511.19575}, url={https://arxiv.org/abs/2511.19575}, } ``` ## 🙏 Acknowledgements We would like to thank [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [MinerU](https://github.com/opendatalab/MinerU), [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR), [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR), [dots.ocr](https://github.com/rednote-hilab/dots.ocr) for their valuable models and ideas. We also appreciate the benchmarks: [OminiDocBench](https://github.com/opendatalab/OmniDocBench), [OCRBench](https://github.com/Yuliang-Liu/MultimodalOCR/tree/main/OCRBench), [DoTA](https://github.com/liangyupu/DIMTDA).

tencent/HunyuanOCR

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