dots.ocr

[![HuggingFace](https://img.shields.io/badge/HuggingFace%20Weights-black.svg?logo=HuggingFace)](https://huggingface.co/rednote-hilab/dots.mocr) [![Arxiv](https://img.shields.io/badge/arXiv-Paper-B31B1B.svg?logo=arxiv)](https://arxiv.org/abs/2512.02498)
🖥️ Live Demo | 💬 WeChat | 📕 rednote | 🐦 X
## Introduction **dots.ocr** Designed for universal accessibility, it possesses the capability to recognize virtually any human script. Beyond achieving state-of-the-art (SOTA) performance in standard multilingual document parsing among models of comparable size, dots.ocr-1.5 excels at converting structured graphics (e.g., charts and diagrams) directly into SVG code, parsing web screens and spotting scene text. ## News * ```2026.03.19 ``` We have rebranded `dots.ocr-1.5` as [dots.mocr](https://github.com/rednote-hilab/dots.mocr). For technical details, please refer to our [paper](https://arxiv.org/abs/2603.13032v1). The model weights are available on Hugging Face: [dots.mocr](https://huggingface.co/rednote-hilab/dots.mocr) and [dots.mocr-svg](https://huggingface.co/rednote-hilab/dots.mocr-svg). * ```2025.10.31 ``` 🚀 We release [dots.ocr.base](https://huggingface.co/rednote-hilab/dots.ocr.base), foundation VLM focus on OCR tasks, also the base model of [dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr). Try it out! * ```2025.07.30 ``` 🚀 We release [dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr), — a multilingual documents parsing model based on 1.7b llm, with SOTA performance. ## Evaluation ### 1. Document Parsing #### 1.1 Elo Score of different bench between latest models
models olmOCR-Bench OmniDocBench (v1.5) XDocParse Average
MonkeyOCR-pro-3B 895.0 811.3 637.1 781.1
GLM-OCR 884.2 972.6 820.7 892.5
PaddleOCR-VL-1.5 897.3 997.9 866.4 920.5
HuanyuanOCR 997.6 1003.9 951.1 984.2
dots.ocr 1041.1 1027.2 1190.3 1086.2
dots.mocr 1104.4 1059.0 1210.7 1124.7
Gemini 3 Pro 1180.4 1128.0 1323.7 1210.7
> **Notes:** > - Results for Gemini 3 Pro, PaddleOCR-VL-1.5, and GLM-OCR were obtained via APIs, while HuanyuanOCR results were generated using local inference. > - The Elo score evaluation was conducted using Gemini 3 Flash. The prompt can be found at: [Elo Score Prompt](tools/elo_score_prompt.py). These results are consistent with the findings on [ocrarena](https://www.ocrarena.ai/battle). #### 1.2 olmOCR-bench
Model ArXiv Old scans math Tables Old scans Headers & footers Multi column Long tiny text Base Overall
Mistral OCR API 77.2 67.5 60.6 29.3 93.6 71.3 77.1 99.4 72.0±1.1
Marker 1.10.1 83.8 66.8 72.9 33.5 86.6 80.0 85.7 99.3 76.1±1.1
MinerU 2.5.4* 76.6 54.6 84.9 33.7 96.6 78.2 83.5 93.7 75.2±1.1
DeepSeek-OCR 77.2 73.6 80.2 33.3 96.1 66.4 79.4 99.8 75.7±1.0
Nanonets-OCR2-3B 75.4 46.1 86.8 40.9 32.1 81.9 93.0 99.6 69.5±1.1
PaddleOCR-VL* 85.7 71.0 84.1 37.8 97.0 79.9 85.7 98.5 80.0±1.0
Infinity-Parser 7B* 84.4 83.8 85.0 47.9 88.7 84.2 86.4 99.8 82.5±?
olmOCR v0.4.0 83.0 82.3 84.9 47.7 96.1 83.7 81.9 99.7 82.4±1.1
Chandra OCR 0.1.0* 82.2 80.3 88.0 50.4 90.8 81.2 92.3 99.9 83.1±0.9
dots.ocr 82.1 64.2 88.3 40.9 94.1 82.4 81.2 99.5 79.1±1.0
dots.mocr 85.9 85.5 90.7 48.2 94.0 85.3 81.6 99.7 83.9±0.9
> **Note:** > - The metrics are from [olmocr](https://github.com/allenai/olmocr), and our own internal evaluations. > - We delete the Page-header and Page-footer cells in the result markdown. #### 1.3 Other Benchmarks
Model Type Methods Size OmniDocBench(v1.5)
TextEdit↓		 OmniDocBench(v1.5)
Read OrderEdit↓		 pdf-parse-bench
GeneralVLMs Gemini-2.5 Pro - 0.075 0.097 9.06
Qwen3-VL-235B-A22B-Instruct 235B 0.069 0.068 9.71
gemini3pro - 0.066 0.079 9.68
SpecializedVLMs Mistral OCR - 0.164 0.144 8.84
Deepseek-OCR 3B 0.073 0.086 8.26
MonkeyOCR-3B 3B 0.075 0.129 9.27
OCRVerse 4B 0.058 0.071 --
MonkeyOCR-pro-3B 3B 0.075 0.128 -
MinerU2.5 1.2B 0.047 0.044 -
PaddleOCR-VL 0.9B 0.035 0.043 9.51
HunyuanOCR 0.9B 0.042 - -
PaddleOCR-VL1.5 0.9B 0.035 0.042 -
GLMOCR 0.9B 0.04 0.043 -
dots.ocr 3B 0.048 0.053 9.29
dots.mocr 3B 0.031 0.029 9.54
> **Note:** > - Metrics are sourced from [OmniDocBench](https://github.com/opendatalab/OmniDocBench) and other model publications. [pdf-parse-bench](https://github.com/phorn1/pdf-parse-bench) results are reproduced by Qwen3-VL-235B-A22B-Instruct. > - Formula and Table metrics for OmniDocBench1.5 are omitted due to their high sensitivity to detection and matching protocols. ### 2. Structured Graphics Parsing Visual languages (e.g., charts, graphics, chemical formulas, logos) encapsulate dense human knowledge. **dots.mocr** unifies the interpretation of these elements by parsing them directly into **SVG code**.
Methods Unisvg Chartmimic Design2Code Genexam SciGen ChemDraw
Low-Level High-Level Score
OCRVerse 0.632 0.852 0.763 0.799 - - - 0.881
Gemini 3 Pro 0.563 0.850 0.735 0.788 0.760 0.756 0.783 0.839
dots.mocr 0.850 0.923 0.894 0.772 0.801 0.664 0.660 0.790
dots.mocr-svg 0.860 0.931 0.902 0.905 0.834 0.8 0.797 0.901
> **Note:** > - We use the ISVGEN metric from [UniSVG](https://ryanlijinke.github.io/) to evaluate the parsing result. For benchmarks that do not natively support image parsing, we use the original images as input, and calculate the ISVGEN score between the rendered output and the original image. > - [OCRVerse](https://github.com/DocTron-hub/OCRVerse) results are derived from various code formats (e.g., SVG, Python), whereas results for Gemini 3 Pro and dots.mocr are based specifically on SVG code. > - Due to the capacity constraints of a 3B-parameter VLM, dots.mocr may not excel in all tasks yet like svg. To complement this, we are simultaneously releasing dots.mocr-svg. We plan to further address these limitations in future updates. ### 3. General Vision Tasks
Model CharXiv_descriptive CharXiv_reasoning OCR_Reasoning infovqa docvqa ChartQA OCRBench AI2D CountBenchQA refcoco
Qwen3vl-2b-instruct 62.3 26.8 - 72.4 93.3 - 85.8 76.9 88.4 -
Qwen3vl-4b-instruct 76.2 39.7 - 80.3 95.3 - 88.1 84.1 84.9 -
dots.mocr 77.4 55.3 22.85 73.76 91.85 83.2 86.0 82.16 94.46 80.03
# Quick Start ## 1. Installation ### Install dots.mocr ```shell conda create -n dots_mocr python=3.12 conda activate dots_mocr git clone https://github.com/rednote-hilab/dots.mocr.git cd dots.mocr # Install pytorch, see https://pytorch.org/get-started/previous-versions/ for your cuda version # pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/cu128 # install flash-attn==2.8.0.post2 for faster inference pip install -e . ``` If you have trouble with the installation, try our [Docker Image](https://hub.docker.com/r/rednotehilab/dots.ocr) for an easier setup, and follow these steps: ### Download Model Weights > 💡**Note:** Please use a directory name without periods (e.g., `DotsMOCR` instead of `dots.mocr`) for the model save path. This is a temporary workaround pending our integration with Transformers. ```shell python3 tools/download_model.py # with modelscope python3 tools/download_model.py --type modelscope ``` ## 2. Deployment ### vLLM inference We highly recommend using vLLM for deployment and inference. **Since vLLM version 0.11.0, Dots OCR has been officially integrated into vLLM with verified performance** and you can use vLLM docker image directly (e.g, `vllm/vllm-openai:v0.11.0`) to deploy the model server. ```shell # Launch vLLM model server ## dots.mocr CUDA_VISIBLE_DEVICES=0 vllm serve rednote-hilab/dots.mocr --tensor-parallel-size 1 --gpu-memory-utilization 0.9 --chat-template-content-format string --served-model-name model --trust-remote-code ## dots.mocr-svg CUDA_VISIBLE_DEVICES=0 vllm serve rednote-hilab/dots.mocr-svg --tensor-parallel-size 1 --gpu-memory-utilization 0.9 --chat-template-content-format string --served-model-name model --trust-remote-code # vLLM API Demo # See dots_mocr/model/inference.py and dots_mocr/utils/prompts.py for details on parameter and prompt settings # that help achieve the best output quality. ## document parsing python3 ./demo/demo_vllm.py --prompt_mode prompt_layout_all_en ## web parsing python3 ./demo/demo_vllm.py --prompt_mode prompt_web_parsing --image_path ./assets/showcase/origin/webpage_1.png ## scene spoting python3 ./demo/demo_vllm.py --prompt_mode prompt_scene_spotting --image_path ./assets/showcase/origin/scene_1.jpg ## image parsing with svg code python3 ./demo/demo_vllm_svg.py --prompt_mode prompt_image_to_svg ## general qa python3 ./demo/demo_vllm_general.py ``` ### Hugginface inference ```shell python3 demo/demo_hf.py ```
Hugginface inference details ```python import torch from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer from qwen_vl_utils import process_vision_info from dots_mocr.utils import dict_promptmode_to_prompt model_path = "./weights/DotsMOCR" model = AutoModelForCausalLM.from_pretrained( model_path, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True ) processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True) image_path = "demo/demo_image1.jpg" prompt = """Please output the layout information from the PDF image, including each layout element's bbox, its category, and the corresponding text content within the bbox. 1. Bbox format: [x1, y1, x2, y2] 2. Layout Categories: The possible categories are ['Caption', 'Footnote', 'Formula', 'List-item', 'Page-footer', 'Page-header', 'Picture', 'Section-header', 'Table', 'Text', 'Title']. 3. Text Extraction & Formatting Rules: - Picture: For the 'Picture' category, the text field should be omitted. - Formula: Format its text as LaTeX. - Table: Format its text as HTML. - All Others (Text, Title, etc.): Format their text as Markdown. 4. Constraints: - The output text must be the original text from the image, with no translation. - All layout elements must be sorted according to human reading order. 5. Final Output: The entire output must be a single JSON object. """ messages = [ { "role": "user", "content": [ { "type": "image", "image": image_path }, {"type": "text", "text": prompt} ] } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(**inputs, max_new_tokens=24000) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ```
### Hugginface inference with CPU Please refer to [CPU inference](https://github.com/rednote-hilab/dots.ocr/issues/1#issuecomment-3148962536) ## 3. Document Parse **Based on vLLM server**, you can parse an image or a pdf file using the following commands: ```bash # Parse all layout info, both detection and recognition # Parse a single image python3 dots_mocr/parser.py demo/demo_image1.jpg # Parse a single PDF python3 dots_mocr/parser.py demo/demo_pdf1.pdf --num_thread 64 # try bigger num_threads for pdf with a large number of pages # Layout detection only python3 dots_mocr/parser.py demo/demo_image1.jpg --prompt prompt_layout_only_en # Parse text only, except Page-header and Page-footer python3 dots_mocr/parser.py demo/demo_image1.jpg --prompt prompt_ocr ``` **Based on Transformers**, you can parse an image or a pdf file using the same commands above, just add `--use_hf true`. > Notice: transformers is slower than vllm, if you want to use demo/* with transformers，just add `use_hf=True` in `DotsMOCRParser(..,use_hf=True)`
Output Results 1. **Structured Layout Data** (`demo_image1.json`): A JSON file containing the detected layout elements, including their bounding boxes, categories, and extracted text. 2. **Processed Markdown File** (`demo_image1.md`): A Markdown file generated from the concatenated text of all detected cells. * An additional version, `demo_image1_nohf.md`, is also provided, which excludes page headers and footers for compatibility with benchmarks like Omnidocbench and olmOCR-bench. 3. **Layout Visualization** (`demo_image1.jpg`): The original image with the detected layout bounding boxes drawn on it.
## 4. Demo Have fun with the [live demo](https://dotsocr.xiaohongshu.com/). ### Examples for document parsing formula1.png table3.png Tibetan.png tradition_zh.png nl.png kannada.png russian.png ### Examples for image parsing svg_1.png svg_2.png svg_4.png svg_5.png svg_6.png > **Note:** > - Inferenced by dots.mocr-svg ### Example for web parsing webpage_1.png webpage_2.png ### Examples for scene spotting scene_1.png scene_2.png # Limitation & Future Work - **Complex Document Elements:** - **Table&Formula**: The extraction of complex tables and mathematical formulas persists as a difficult task given the model's compact architecture. - **Picture**: We have adopted an SVG code representation for parsing structured graphics; however, the performance has yet to achieve the desired level of robustness. - **Parsing Failures:** While we have reduced the rate of parsing failures compared to the previous version, these issues may still occur occasionally. We remain committed to further resolving these edge cases in future updates. # Citation ```BibTeX @misc{zheng2026multimodalocrparsedocuments, title={Multimodal OCR: Parse Anything from Documents}, author={Handong Zheng and Yumeng Li and Kaile Zhang and Liang Xin and Guangwei Zhao and Hao Liu and Jiayu Chen and Jie Lou and Jiyu Qiu and Qi Fu and Rui Yang and Shuo Jiang and Weijian Luo and Weijie Su and Weijun Zhang and Xingyu Zhu and Yabin Li and Yiwei ma and Yu Chen and Zhaohui Yu and Guang Yang and Colin Zhang and Lei Zhang and Yuliang Liu and Xiang Bai}, year={2026}, eprint={2603.13032}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2603.13032}, } ``` ```BibTeX @misc{li2025dotsocrmultilingualdocumentlayout, title={dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model}, author={Yumeng Li and Guang Yang and Hao Liu and Bowen Wang and Colin Zhang}, year={2025}, eprint={2512.02498}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2512.02498}, } ```