dots.ocr/assets/blog.md

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

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, 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 on Document Parsing Benchmarks

Notes:

• The EN, ZH metrics are the end2end evaluation results of OmniDocBench, and Multilingual metric is the end2end evaluation results of dots.ocr-bench.

Show Case

Example for formula document

Example for table document

Example for multilingual document

Example for reading order

Example for grounding ocr

Benchmark Results

1. OmniDocBench

The end-to-end evaluation results of different tasks.

The end-to-end text recognition performance across 9 PDF page types.

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

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, 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.
• 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

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, olmocr, and our own internal evaluations.
• We delete the Page-header and Page-footer cells in the result markdown.

Methods

Pretrain

We developed a foundational Vision-Language Model (VLM) through a three-stage training process:

• Stage1: Vision Encoder Pre-training We trained a 1.2-billion-parameter Vision Encoder (VE) from scratch on a vast and comprehensive dataset of image-text pairs.
• Stage2: VE Continued Pre-training We incorporated additional visual data, including OCR, video, grounding data, etc. Leveraging the NaViT architecture, our model supports high-resolution inputs of up to 11 million pixels. The VE was then aligned with the Qwen2.5-1.5B language model and trained on this diverse visual data with LLM frozen, which resulted in our general vision encoder dots.vit.
• Stage3: VLM Specialization for OCR We then used a pure OCR dataset for training. To improve training efficiency, we first trained on a certain volume of tokens with the VE parameters frozen. Subsequently, we unfroze all parameters and continued training on an additional one-fifth of that token volume, which produced our foundational OCR model, dots.ocr.base.

SFT

The SFT stage was implemented on the following key strategies:

• Diverse SFT Dataset: We constructed a dataset of nearly 300,000 samples, integrating our in-house manual annotations, synthetic data (tables, formulas, multilingual OCR), as well as open-source datasets.
• Iterative Data Flywheel: We employed a feedback loop to build an inhouse multilingual structured layout data with 15k samples. This process, repeated over three iterations, involved:
  • Sampling "bad cases" based on model performance.
  • Manually annotating these cases.
  • Adding them back into the training set.
• Reading Order: We corrected the sequence of all layout element boxes to establish the correct reading order. This was primarily done using larger models for sorting, supplemented by rule-based post-processing methods. We found that with sufficient data diversity and quality, training the model on a list of elements sorted in their natural reading order yields excellent results.
• Quality and Robustness: We build a multi-expert system for data cleaning and distillation, and applied data augmentation (resizing, rotation, noise) to improve model robustness.
• Multitask training: We leveraged a single source of structured layout data to generate the SFT data with a variety of prompts. This approach enables the model to perform different tasks, such as detection and recognition, based on the specific prompt provided.

The resulting dots.ocr model demonstrates performance on par with models possessing significantly more parameters.

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).
    

• 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].

Author List

Contributors

Mi Jian, Yumeng Li, Bowen Wang, Xiaomin He, Zheyuan Gu

Project Leader

Qing Yan

Advisor

Colin Zhang, Lei Zhang