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

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🖥️ 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↓

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