README.md

---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- ocr
- vision-language-model
- document-understanding
---

# OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

OCRVerse is a holistic OCR method that enables unified text-centric OCR (extracting text from documents like books and magazines) and vision-centric OCR (identifying visual elements from information-dense sources like charts, web pages, and scientific plots) in an end-to-end manner.

- **Paper:** [OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models](https://huggingface.co/papers/2601.21639)
- **GitHub Repository:** [DocTron-hub/OCRVerse](https://github.com/DocTron-hub/OCRVerse)

## Usage Example

To use OCRVerse, please ensure you have the `transformers` library installed:

```shell
pip install "transformers>=4.57.0"
```

### Text-Centric Document Parsing

Below is a simple example of how to use OCRVerse for document parsing tasks.

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
import torch

# Load model
model_path = 'DocTron/OCRVerse'
model = Qwen3VLForConditionalGeneration.from_pretrained(
    model_path,
    dtype="auto", 
    device_map="cuda",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)

# Prepare input with image and text
image_path = "path/to/your/image.jpg"
# We recommend using the following prompt for better performance
prompt = "Extract the main content from the document in the image, keeping the original structure. Convert all formulas to LaTeX and all tables to HTML."

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image_path},
            {"type": "text", "text": prompt},
        ]
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages, 
    tokenize=True, 
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=8192, do_sample=False)

generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
```

## Citation

If you find this project useful, please cite our paper:

```bibtex
@article{zhong2026ocrverse,
  title={OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models},
  author={Yufeng Zhong and Lei Chen and Xuanle Zhao and Wenkang Han and Liming Zheng and Jing Huang and Deyang Jiang and Yilin Cao and Lin Ma and Zhixiong Zeng},
  journal={arXiv preprint arXiv:2601.21639},
  year={2026}
}
```