--- license: mit library_name: dots_ocr pipeline_tag: image-text-to-text tags: - image-to-text - ocr - document-parse - layout - table - formula - transformers - custom_code language: - en - zh - multilingual ---

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

[![Blog](https://img.shields.io/badge/Blog-View_on_GitHub-333.svg?logo=github)](https://github.com/rednote-hilab/dots.ocr/blob/master/assets/blog.md) [![HuggingFace](https://img.shields.io/badge/HuggingFace%20Weights-black.svg?logo=HuggingFace)](https://huggingface.co/rednote-hilab/dots.ocr)

🖥️ Live Demo | 💬 WeChat | 📕 rednote

## 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. ## Usage with transformers ```py 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) ``` ### 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	Overall^Edit↓		Text^Edit↓		Formula^Edit↓		Table^TEDS↑		Table^Edit↓		Read Order^Edit↓
Model Type	Methods	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	Overall^Edit↓	Text^Edit↓	Formula^Edit↓	Table^TEDS↑	Table^Edit↓	Read Order^Edit↓
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↑
Method	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/master/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	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 python3 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/master/docker/Dockerfile) to build the deployment environment by yourself. ```shell # You need to register model to vllm at first python3 tools/download_model.py export hf_model_path=./weights/DotsOCR # Path to your downloaded model weights, 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. 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` # If you downloaded model weights by yourself, please replace `DotsOCR` by your model saved directory name, and remember to use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`) # 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 # If you get a ModuleNotFoundError: No module named 'DotsOCR', please check the note above on the saved model directory name. # 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

### Example for table document

### Example for multilingual document

### Example for reading order

### Example for grounding ocr

## 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/master/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].

rednote-hilab/dots.ocr

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