File size: 1,290 Bytes
87f0948 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
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"]
|