🎯 Demo | 📥 Model Download | 📄 Technical Report | 🌟 Github

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.

pip install git+https://github.com/huggingface/transformers@82a06db03535c49aa987719ed0746a76093b1ec4

Note: We will merge it into the Transformers main branch later.

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": "user",
        "content": [
            {"type": "image", "image": img_path},
            {"type": "text", "text": (
                "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."
            )},
        ],
    }
]
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)

Checkout vLLM HunyuanOCR Usage Guide.

Using pip:

pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

Using uv:

uv pip install vllm --extra-index-url https://wheels.vllm.ai/nightly

from vllm import LLM, SamplingParams
from PIL import Image
from transformers import AutoProcessor

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": "user", "content": [
        {"type": "image", "image": img_path},
        {"type": "text", "text": "Detect and recognize text in the image, and output the text coordinates in a formatted manner."}
    ]}
]
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(output.outputs[0].text)

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。

@software{hunyuanocr2025,
  author    = {Tencent Hunyuan Vision Team},
  title     = {HunyuanOCR Technical Report},
  year      = {2025},
  url       = {https://github.com/Tencent-Hunyuan/HunyuanOCR},
  publisher = {GitHub}
}

We would like to thank PaddleOCR, MinerU, MonkeyOCR, DeepSeek-OCR, dots.ocr for their valuable models and ideas. We also appreciate the benchmarks: OminiDocBench, OCRBench, DoTA.