dots.ocr

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.

Usage with transformers

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, and Multilingual metric is the end2end evaluation results of dots.ocr-bench.

News

• 2025.07.30 🚀 We release 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.

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

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.

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

Notes:

• prompt_layout_all_en for parse all, prompt_layout_only_en for detection only, please refer to prompts

3. olmOCR-bench.

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.

Quick Start

1. Installation

Install dots.ocr