OvisOCR2

OvisOCR2 by ATH-MaaS, a image-text-to-text model with OCR, multimodal capabilities. Understand and compare OCR, multimodal features, benchmarks, and capabilities.

Comparison

Feature	OvisOCR2	Interfaze
Input Modalities	text, image	image, text, audio, video, document
Native OCR	Yes	Yes
Long Document Processing	No	Yes
Language Support	unknown	162+
Native Speech-to-Text	No	Yes
Native Object Detection	No	Yes
Guardrail Controls	No	Yes
Context Input Size	unknown	1M
Tool Calling	No	Tool calling supported + built in browser, code execution and web search

OCR Capabilities

Feature	OvisOCR2	Interfaze
Text Bounding Boxes	Partial	Yes
Confidence Scores	No	Yes
Dense Image Processing	Yes	Yes
Low Quality Images	Partial	Yes
Handwritten Text	No	Yes
Charts, Tables & Equations	Partial	Yes

Scaling

Feature	OvisOCR2	Interfaze
Scaling	Self-hosted/Provider-hosted with quantization	Unlimited

View model card on Hugging Face

Introduction

We are pleased to announce the release of OvisOCR2, a compact 0.8B end-to-end model for page-level document parsing. Given a document page image, OvisOCR2 generates a Markdown representation in natural reading order, covering text, formulas, tables, and visual regions.

OvisOCR2 is developed by post-training Qwen3.5-0.8B using a carefully designed data engine that combines real-world and synthetic data, together with a multi-stage training recipe integrating SFT, RL, and OPD. The model delivers strong document parsing performance while maintaining a small deployment footprint.

OvisOCR2 achieves an overall score of 96.58 on OmniDocBench v1.6, establishing a new state of the art and becoming the first end-to-end model to top this leaderboard previously dominated by pipeline methods. On PureDocBench, OvisOCR2 also achieves the highest Avg3 score of 75.06.

Performance

Inference

pip install "vllm==0.22.1" pillow

from PIL import Image
from vllm import LLM, SamplingParams


class OvisOCR2Parser:
    def __init__(self, model_name_or_path: str):
        self.model = LLM(
            model=model_name_or_path,
            tensor_parallel_size=1,
            gpu_memory_utilization=0.8,
            gdn_prefill_backend="triton"
        )

        prompt = '\nExtract all readable content from the image in natural human reading order and output the result as a single Markdown document. For charts or images, represent them using an HTML image tag: <' + 'img src="images/bbox_{left}_{top}_{right}_{bottom}.jpg" />, where left, top, right, bottom are bounding box coordinates scaled to [0, 1000). Format formulas as LaTeX. Format tables as HTML: <table>...</table>. Transcribe all other text as standard Markdown. Preserve the original text without translation or paraphrasing.'
        self.prompt = self.model.get_tokenizer().apply_chat_template(
            [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt}]}],
            tokenize=False,
            add_generation_prompt=True,
            enable_thinking=False
        )

        self.sampling_params = SamplingParams(
            max_tokens=16384,
            temperature=0.0
        )

    def _clean_truncated_repeats(
        self,
        text: str,
        min_text_len: int = 8000,
        max_period: int = 200,
        min_period: int = 1,
        min_repeat_chars: int = 100,
        min_repeat_times: int = 5
    ) -> str:
        n = len(text)
        if n < min_text_len:
            return text

        max_period = min(max_period, n - 1)
        for unit_len in range(min_period, max_period + 1):
            if text[n - 1] != text[n - 1 - unit_len]:
                continue

            match_len = 1
            idx = n - 2
            while idx >= unit_len and text[idx] == text[idx - unit_len]:
                match_len += 1
                idx -= 1

            total_len = match_len + unit_len
            repeat_times = total_len // unit_len
            tail_len = total_len % unit_len

            if repeat_times >= min_repeat_times and total_len >= min_repeat_chars:
                return text[: n - total_len + unit_len] + text[n - tail_len:]

        return text

    def parse(self, images: list[Image.Image], filter_imgtags: bool = True) -> list[str]:
        vllm_inputs = [
            {
                "prompt": self.prompt,
                "multi_modal_data": {"image": image},
                "mm_processor_kwargs": {
                    "images_kwargs": {
                        "min_pixels": 448 * 448,
                        "max_pixels": 2880 * 2880
                    }
                }
            }
            for image in images
        ]

        outputs = self.model.generate(vllm_inputs, self.sampling_params)

        markdowns = []
        for output in outputs:
            text = output.outputs[0].text.strip()
            if filter_imgtags:
                text = "\n\n".join(
                    block
                    for block in text.split("\n\n")
                    if not block.strip().startswith('<img src="images/bbox_')
                )
            markdowns.append(self._clean_truncated_repeats(text))

        return markdowns


if __name__ == "__main__":
    parser = OvisOCR2Parser("ATH-MaaS/OvisOCR2")
    images = [Image.open("test1.jpg"), Image.open("test2.jpg")]
    markdowns = parser.parse(images)
    print(markdowns[0])

By default, parse removes HTML image tags for visual regions. To render Markdown with visual regions, set filter_imgtags=False and save the Markdown file together with the referenced image crops as follows:

import re
from pathlib import Path

from PIL import Image


BBOX_IMAGE_PATTERN = re.compile(
    r'<img src='https://huggingface.co/ATH-MaaS/OvisOCR2/resolve/main/+ r'"images/bbox_(\d+)_(\d+)_(\d+)_(\d+)\.jpg" />'
)


def save_renderable_markdown_with_visual_regions(
    markdown: str,
    page_image: Image.Image,
    output_dir: str,
) -> None:
    output_dir = Path(output_dir)
    images_dir = output_dir / "images"
    images_dir.mkdir(parents=True, exist_ok=True)

    width, height = page_image.size
    for left, top, right, bottom in BBOX_IMAGE_PATTERN.findall(markdown):
        x1 = max(0, min(width, round(int(left) * width / 1000)))
        y1 = max(0, min(height, round(int(top) * height / 1000)))
        x2 = max(0, min(width, round(int(right) * width / 1000)))
        y2 = max(0, min(height, round(int(bottom) * height / 1000)))
        if x2 <= x1 or y2 <= y1:
            continue

        crop_path = images_dir / f"bbox_{left}_{top}_{right}_{bottom}.jpg"
        page_image.crop((x1, y1, x2, y2)).convert("RGB").save(crop_path)

    (output_dir / "output.md").write_text(markdown, encoding="utf-8")


parser = OvisOCR2Parser("ATH-MaaS/OvisOCR2")
page_image = Image.open("test1.jpg")
markdown = parser.parse([page_image], filter_imgtags=False)[0]
save_renderable_markdown_with_visual_regions(markdown, page_image, "output")

Citation

If you find OvisOCR2 useful, please consider citing our technical report:

@misc{lu2026ovisocr2,
  title        = {{OvisOCR2 Technical Report}},
  author       = {Lu, Shiyin and Li, Yinglun and Xia, Yu and Chen, Yuhui and Ji, An-Yang and Jiang, Jun-Peng and Chen, Qing-Guo and Zhao, Jianshan and Lin, En and Li, Haijun and Qin, Cheng and Xu, Zhao and Luo, Weihua},
  year         = {2026}
}

License

This project is licensed under the Apache License, Version 2.0 (SPDX-License-Identifier: Apache-2.0).

Disclaimer

We used filtering and quality-assurance procedures during data construction to reduce parsing errors such as repeated outputs, incomplete content, invalid table/formula structures, and reading-order inconsistencies. Due to the diversity and complexity of real-world documents, OvisOCR2 may still produce incorrect or incomplete outputs. Please manually verify results in critical applications.