General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
[GitHub](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/tree/main)
[Haoran Wei*](https://scholar.google.com/citations?user=J4naK0MAAAAJ&hl=en), Chenglong Liu*, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, [Zheng Ge](https://joker316701882.github.io/), Liang Zhao, [Jianjian Sun](https://scholar.google.com/citations?user=MVZrGkYAAAAJ&hl=en), [Yuang Peng](https://scholar.google.com.hk/citations?user=J0ko04IAAAAJ&hl=zh-CN&oi=ao), Chunrui Han, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en)
## Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
```
torch==2.0.1
torchvision==0.15.2
transformers==4.37.2
megfile==3.1.2
```
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
model = model.eval().cuda()
# input your test image
image_file = 'xxx.jpg'
# plain texts OCR
model.chat(tokenizer, image_file, ocr_type='ocr')
# format texts OCR:
model.chat(tokenizer, image_file, ocr_type='format')
# fine-grained OCR:
model.chat(tokenizer, image_file, ocr_type='ocr', ocr_box='')
model.chat(tokenizer, image_file, ocr_type='format', ocr_box='')
model.chat(tokenizer, image_file, ocr_type='ocr', ocr_color='')
model.chat(tokenizer, image_file, ocr_type='format', ocr_color='')
# multi-crop OCR:
res = model.chat_crop(tokenizer, image_file = image_file)
# render the formatted OCR results:
model.chat(tokenizer, image_file, ocr_type='format', ocr_box='', ocr_color='', render=True, save_render_file = './demo.html')
print(res)
```
https://huggingface.co/ucaslcl/GOT-OCR2_0/tree/main/assets#:~:text=a%20minute%20ago-,got_logo.png
