| title = """# GOT-OCR 2.0: Transformers 🤗 implementation demo""" | |
| description = """ | |
| This demo utilizes the **Transformers implementation of GOT-OCR 2.0** to extract text from images. | |
| The GOT-OCR 2.0 model was introduced in the paper: | |
| [**General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model**](https://arxiv.org/abs/2409.01704) | |
| by *Haoran Wei, Chenglong Liu, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, Zheng Ge, Liang Zhao, Jianjian Sun, Yuang Peng, Chunrui Han, and Xiangyu Zhang*. | |
| ### Key Features | |
| GOT-OCR 2.0 is a **state-of-the-art OCR model** designed to handle a wide variety of tasks, including: | |
| - **Plain Text OCR** | |
| - **Formatted Text OCR** | |
| - **Fine-grained OCR** | |
| - **Multi-crop OCR** | |
| - **Multi-page OCR** | |
| ### Beyond Text | |
| GOT-OCR 2.0 has also been fine-tuned to work with non-textual data, such as: | |
| - **Charts and Tables** | |
| - **Math and Molecular Formulas** | |
| - **Geometric Shapes** | |
| - **Sheet Music** | |
| Explore the capabilities of this cutting-edge model through this interactive demo! | |
| """ | |
| tasks = [ | |
| "Plain Text OCR", | |
| "Format Text OCR", | |
| "Fine-grained OCR (Box)", | |
| "Fine-grained OCR (Color)", | |
| "Multi-crop OCR", | |
| "Multi-page OCR", | |
| ] | |
| ocr_types = ["ocr", "format"] | |
| ocr_colors = ["red", "green", "blue"] | |