OvisOCR2
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=' + 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:
@article{lu2026ovisocr2,
title={OvisOCR2 Technical Report},
author={Shiyin Lu and Yinglun Li and Yu Xia and Yuhui Chen and An-Yang Ji and Jun-Peng Jiang and Qing-Guo Chen and Jianshan Zhao and En Lin and Haijun Li and Cheng Qin and Zhao Xu and Weihua Luo},
journal={arXiv preprint arXiv:2607.13639},
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.