dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model

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## Introduction **dots.ocr** is a powerful, multilingual document parser that unifies layout detection and content recognition within a single vision-language model while maintaining good reading order. Despite its compact 1.7B-parameter LLM foundation, it achieves state-of-the-art(SOTA) performance. 1. **Powerful Performance:** **dots.ocr** achieves SOTA performance for text, tables, and reading order on [OmniDocBench](https://github.com/opendatalab/OmniDocBench), while delivering formula recognition results comparable to much larger models like Doubao-1.5 and gemini2.5-pro. 2. **Multilingual Support:** **dots.ocr** demonstrates robust parsing capabilities for low-resource languages, achieving decisive advantages across both layout detection and content recognition on our in-house multilingual documents benchmark. 3. **Unified and Simple Architecture:** By leveraging a single vision-language model, **dots.ocr** offers a significantly more streamlined architecture than conventional methods that rely on complex, multi-model pipelines. Switching between tasks is accomplished simply by altering the input prompt, proving that a VLM can achieve competitive detection results compared to traditional detection models like DocLayout-YOLO. 4. **Efficient and Fast Performance:** Built upon a compact 1.7B LLM, **dots.ocr** provides faster inference speeds than many other high-performing models based on larger foundations. ### Performance Comparison: dots.ocr vs. Competing Models > **Notes:** > - The EN, ZH metrics are the end2end evaluation results of [OmniDocBench](https://github.com/opendatalab/OmniDocBench), and Multilingual metric is the end2end evaluation results of dots.ocr-bench. ## News * ```2025.07.30 ``` 🚀 We release [dots.ocr](https://github.com/rednote-hilab/dots.ocr), — a multilingual documents parsing model based on 1.7b llm, with SOTA performance. ## Benchmark Results ### 1. OmniDocBench #### The end-to-end evaluation results of different tasks.
Model
Type		 Methods OverallEdit↓ TextEdit↓ FormulaEdit↓ TableTEDS↑ TableEdit↓ Read OrderEdit↓
EN ZH EN ZH EN ZH EN ZH EN ZH EN ZH
Pipeline
Tools		 MinerU 0.150 0.357 0.061 0.215 0.278 0.577 78.6 62.1 0.180 0.344 0.079 0.292
Marker 0.336 0.556 0.080 0.315 0.530 0.883 67.6 49.2 0.619 0.685 0.114 0.340
Mathpix 0.191 0.365 0.105 0.384 0.306 0.454 77.0 67.1 0.243 0.320 0.108 0.304
Docling 0.589 0.909 0.416 0.987 0.999 1 61.3 25.0 0.627 0.810 0.313 0.837
Pix2Text 0.320 0.528 0.138 0.356 0.276 0.611 73.6 66.2 0.584 0.645 0.281 0.499
Unstructured 0.586 0.716 0.198 0.481 0.999 1 0 0.06 1 0.998 0.145 0.387
OpenParse 0.646 0.814 0.681 0.974 0.996 1 64.8 27.5 0.284 0.639 0.595 0.641
PPStruct-V3 0.145 0.206 0.058 0.088 0.295 0.535 - - 0.159 0.109 0.069 0.091
Expert
VLMs		 GOT-OCR 0.287 0.411 0.189 0.315 0.360 0.528 53.2 47.2 0.459 0.520 0.141 0.280
Nougat 0.452 0.973 0.365 0.998 0.488 0.941 39.9 0 0.572 1.000 0.382 0.954
Mistral OCR 0.268 0.439 0.072 0.325 0.318 0.495 75.8 63.6 0.600 0.650 0.083 0.284
OLMOCR-sglang 0.326 0.469 0.097 0.293 0.455 0.655 68.1 61.3 0.608 0.652 0.145 0.277
SmolDocling-256M 0.493 0.816 0.262 0.838 0.753 0.997 44.9 16.5 0.729 0.907 0.227 0.522
Dolphin 0.206 0.306 0.107 0.197 0.447 0.580 77.3 67.2 0.180 0.285 0.091 0.162
MinerU 2 0.139 0.240 0.047 0.109 0.297 0.536 82.5 79.0 0.141 0.195 0.069< 0.118
OCRFlux 0.195 0.281 0.064 0.183 0.379 0.613 71.6 81.3 0.253 0.139 0.086 0.187
MonkeyOCR-pro-3B 0.138 0.206 0.067 0.107 0.246 0.421 81.5 87.5 0.139 0.111 0.100 0.185
General
VLMs		 GPT4o 0.233 0.399 0.144 0.409 0.425 0.606 72.0 62.9 0.234 0.329 0.128 0.251
Qwen2-VL-72B 0.252 0.327 0.096 0.218 0.404 0.487 76.8 76.4 0.387 0.408 0.119 0.193
Qwen2.5-VL-72B 0.214 0.261 0.092 0.18 0.315 0.434 82.9 83.9 0.341 0.262 0.106 0.168
Gemini2.5-Pro 0.148 0.212 0.055 0.168 0.356 0.439 85.8 86.4 0.13 0.119 0.049 0.121
doubao-1-5-thinking-vision-pro-250428 0.140 0.162 0.043 0.085 0.295 0.384 83.3 89.3 0.165 0.085 0.058 0.094
Expert VLMs dots.ocr 0.125 0.160 0.032 0.066 0.329 0.416 88.6 89.0 0.099 0.092 0.040 0.067
#### The end-to-end text recognition performance across 9 PDF page types.
Model
Type		 Models Book Slides Financial
Report		 Textbook Exam
Paper		 Magazine Academic
Papers		 Notes Newspaper Overall
Pipeline
Tools		 MinerU 0.055 0.124 0.033 0.102 0.159 0.072 0.025 0.984 0.171 0.206
Marker 0.074 0.340 0.089 0.319 0.452 0.153 0.059 0.651 0.192 0.274
Mathpix 0.131 0.220 0.202 0.216 0.278 0.147 0.091 0.634 0.690 0.300
Expert
VLMs		 GOT-OCR 0.111 0.222 0.067 0.132 0.204 0.198 0.179 0.388 0.771 0.267
Nougat 0.734 0.958 1.000 0.820 0.930 0.830 0.214 0.991 0.871 0.806
Dolphin 0.091 0.131 0.057 0.146 0.231 0.121 0.074 0.363 0.307 0.177
OCRFlux 0.068 0.125 0.092 0.102 0.119 0.083 0.047 0.223 0.536 0.149
MonkeyOCR-pro-3B 0.084 0.129 0.060 0.090 0.107 0.073 0.050 0.171 0.107 0.100
General
VLMs		 GPT4o 0.157 0.163 0.348 0.187 0.281 0.173 0.146 0.607 0.751 0.316
Qwen2.5-VL-7B 0.148 0.053 0.111 0.137 0.189 0.117 0.134 0.204 0.706 0.205
InternVL3-8B 0.163 0.056 0.107 0.109 0.129 0.100 0.159 0.150 0.681 0.188
doubao-1-5-thinking-vision-pro-250428 0.048 0.048 0.024 0.062 0.085 0.051 0.039 0.096 0.181 0.073
Expert VLMs dots.ocr 0.031 0.047 0.011 0.082 0.079 0.028 0.029 0.109 0.056 0.055
> **Notes:** > - The metrics are from [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR), [OmniDocBench](https://github.com/opendatalab/OmniDocBench), and our own internal evaluations. > - We delete the Page-header and Page-footer cells in the result markdown. > - We use tikz_preprocess pipeline to upsample the images to dpi 200. ### 2. **dots.ocr-bench** This is an inhouse benchmark which contain 1493 pdf images with 100 languages. #### The end-to-end evaluation results of different tasks.
Methods OverallEdit↓ TextEdit↓ FormulaEdit↓ TableTEDS↑ TableEdit↓ Read OrderEdit↓
MonkeyOCR-3B 0.483 0.445 0.627 50.93 0.452 0.409
doubao-1-5-thinking-vision-pro-250428 0.291 0.226 0.440 71.2 0.260 0.238
doubao-1-6 0.299 0.270 0.417 71.0 0.258 0.253
Gemini2.5-Pro 0.251 0.163 0.402 77.1 0.236 0.202
dots.ocr 0.177 0.075 0.297 79.2 0.186 0.152
> **Notes:** > - We use the same metric calculation pipeline of [OmniDocBench](https://github.com/opendatalab/OmniDocBench). > - We delete the Page-header and Page-footer cells in the result markdown. #### Layout Detection
Method F1@IoU=.50:.05:.95↑ F1@IoU=.50↑
Overall Text Formula Table Picture Overall Text Formula Table Picture
DocLayout-YOLO-DocStructBench 0.733 0.694 0.480 0.803 0.619 0.806 0.779 0.620 0.858 0.678
dots.ocr-parse all 0.831 0.801 0.654 0.838 0.748 0.922 0.909 0.770 0.888 0.831
dots.ocr-detection only 0.845 0.816 0.716 0.875 0.765 0.930 0.917 0.832 0.918 0.843
> **Notes:** > - prompt_layout_all_en for **parse all**, prompt_layout_only_en for **detection only**, please refer to [prompts](https://github.com/rednote-hilab/dots.ocr/blob/main/dots_ocr/utils/prompts.py) ### 3. olmOCR-bench.
Model ArXiv Old Scans
Math		 Tables Old Scans Headers and
Footers		 Multi
column		 Long Tiny
Text		 Base Overall
GOT OCR 52.7 52.0 0.2 22.1 93.6 42.0 29.9 94.0 48.3 ± 1.1
Marker 76.0 57.9 57.6 27.8 84.9 72.9 84.6 99.1 70.1 ± 1.1
MinerU 75.4 47.4 60.9 17.3 96.6 59.0 39.1 96.6 61.5 ± 1.1
Mistral OCR 77.2 67.5 60.6 29.3 93.6 71.3 77.1 99.4 72.0 ± 1.1
Nanonets OCR 67.0 68.6 77.7 39.5 40.7 69.9 53.4 99.3 64.5 ± 1.1
GPT-4o
(No Anchor)		 51.5 75.5 69.1 40.9 94.2 68.9 54.1 96.7 68.9 ± 1.1
GPT-4o
(Anchored)		 53.5 74.5 70.0 40.7 93.8 69.3 60.6 96.8 69.9 ± 1.1
Gemini Flash 2
(No Anchor)		 32.1 56.3 61.4 27.8 48.0 58.7 84.4 94.0 57.8 ± 1.1
Gemini Flash 2
(Anchored)		 54.5 56.1 72.1 34.2 64.7 61.5 71.5 95.6 63.8 ± 1.2
Qwen 2 VL
(No Anchor)		 19.7 31.7 24.2 17.1 88.9 8.3 6.8 55.5 31.5 ± 0.9
Qwen 2.5 VL
(No Anchor)		 63.1 65.7 67.3 38.6 73.6 68.3 49.1 98.3 65.5 ± 1.2
olmOCR v0.1.75
(No Anchor)		 71.5 71.4 71.4 42.8 94.1 77.7 71.0 97.8 74.7 ± 1.1
olmOCR v0.1.75
(Anchored)		 74.9 71.2 71.0 42.2 94.5 78.3 73.3 98.3 75.5 ± 1.0
MonkeyOCR-pro-3B [Demo] 83.8 68.8 74.6 36.1 91.2 76.6 80.1 95.3 75.8 ± 1.0
dots.ocr 82.1 64.2 88.3 40.9 94.1 82.4 81.2 99.5 79.1 ± 1.0
> **Note:** > - The metrics are from [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR), [olmocr](https://github.com/allenai/olmocr), and our own internal evaluations. > - We delete the Page-header and Page-footer cells in the result markdown. # Quick Start ## 1. Installation ### Install dots.ocr ```shell conda create -n dots_ocr python=3.12 conda activate dots_ocr git clone https://github.com/rednote-hilab/dots.ocr.git cd dots.ocr # 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 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: ```shell git clone https://github.com/rednote-hilab/dots.ocr.git cd dots.ocr pip install -e . ``` ### Download Model Weights > 💡**Note:** Please use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`) for the model save path. This is a temporary workaround pending our integration with Transformers. ```shell python tools/download_model.py ``` ## 2. Deployment ### vLLM inference We highly recommend using vllm for deployment and inference. All of our evaluations results are based on vllm version 0.9.1. The [Docker Image](https://hub.docker.com/r/rednotehilab/dots.ocr) is based on the official vllm image. You can also follow [Dockerfile](https://github.com/rednote-hilab/dots.ocr/blob/main/docker/Dockerfile) to build the deployment environment by yourself. ```shell # You need to register model to vllm at first hf_model_path=./weights/DotsOCR # Path to your downloaded model weights export PYTHONPATH=$(dirname "$hf_model_path"):$PYTHONPATH sed -i '/^from vllm\.entrypoints\.cli\.main import main$/a\ from DotsOCR import modeling_dots_ocr_vllm' `which vllm` # launch vllm server CUDA_VISIBLE_DEVICES=0 vllm serve ${hf_model_path} --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --chat-template-content-format string --served-model-name model --trust-remote-code # vllm api demo python3 ./demo/demo_vllm.py --prompt_mode prompt_layout_all_en ``` ### 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_ocr.utils import dict_promptmode_to_prompt model_path = "./weights/DotsOCR" 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) ```
## 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_ocr/parser.py demo/demo_image1.jpg # Parse a single PDF python3 dots_ocr/parser.py demo/demo_pdf1.pdf --num_threads 64 # try bigger num_threads for pdf with a large number of pages # Layout detection only python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_layout_only_en # Parse text only, except Page-header and Page-footer python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_ocr # Parse layout info by bbox python3 dots_ocr/parser.py demo/demo_image1.jpg --prompt prompt_grounding_ocr --bbox 163 241 1536 705 ```
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 You can run the demo with the following command, or try directly at [live demo](https://dotsocr.xiaohongshu.com/) ```bash python demo/demo_gradio.py ``` We also provide a demo for grounding ocr: ```bash python demo/demo_gradio_annotion.py ``` ### Example for formula document formula1.png formula2.png formula3.png ### Example for table document table1.png table2.png table3.png ### Example for multilingual document Tibetan.png tradition_zh.png nl.png kannada.png russian.png ### Example for reading order reading_order.png ### Example for grounding ocr grounding.png ## Acknowledgments We would like to thank [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [aimv2](https://github.com/apple/ml-aim), [MonkeyOCR](https://github.com/Yuliang-Liu/MonkeyOCR), [OmniDocBench](https://github.com/opendatalab/OmniDocBench), [PyMuPDF](https://github.com/pymupdf/PyMuPDF), for providing code and models. We also thank [DocLayNet](https://github.com/DS4SD/DocLayNet), [M6Doc](https://github.com/HCIILAB/M6Doc), [CDLA](https://github.com/buptlihang/CDLA), [D4LA](https://github.com/AlibabaResearch/AdvancedLiterateMachinery) for providing valuable datasets. ## Limitation & Future Work - **Complex Document Elements:** - **Table&Formula**: dots.ocr is not yet perfect for high-complexity tables and formula extraction. - **Picture**: Pictures in documents are currently not parsed. - **Parsing Failures:** The model may fail to parse under certain conditions: - When the character-to-pixel ratio is excessively high. Try enlarging the image or increasing the PDF parsing DPI (a setting of 200 is recommended). However, please note that the model performs optimally on images with a resolution under 11289600 pixels. - Continuous special characters, such as ellipses (`...`) and underscores (`_`), may cause the prediction output to repeat endlessly. In such scenarios, consider using alternative prompts like `prompt_layout_only_en`, `prompt_ocr`, or `prompt_grounding_ocr` ([details here](https://github.com/rednote-hilab/dots.ocr/blob/main/dots_ocr/utils/prompts.py)). - **Performance Bottleneck:** Despite its 1.7B parameter LLM foundation, **dots.ocr** is not yet optimized for high-throughput processing of large PDF volumes. We are committed to achieving more accurate table and formula parsing, as well as enhancing the model's OCR capabilities for broader generalization, all while aiming for **a more powerful, more efficient model**. Furthermore, we are actively considering the development of **a more general-purpose perception model** based on Vision-Language Models (VLMs), which would integrate general detection, image captioning, and OCR tasks into a unified framework. **Parsing the content of the pictures in the documents** is also a key priority for our future work. We believe that collaboration is the key to tackling these exciting challenges. If you are passionate about advancing the frontiers of document intelligence and are interested in contributing to these future endeavors, we would love to hear from you. Please reach out to us via email at: [yanqing4@xiaohongshu.com].