Demo of GOT-OCR 2.0's Transformers implementation
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GOT OCR Transformers is a demo implementation of the GOT-OCR 2.0 model using state-of-the-art Transformer architecture. It is designed to extract text from images efficiently using Optical Character Recognition (OCR) techniques. This tool leverages advanced deep learning models to deliver high accuracy and performance in text recognition tasks.
pip install got-ocr-transformers
from got_ocr-transformers import GOTOCR
model = GOTOCR.from_pretrained('got-ocr')
image = PIL.Image.open('image.jpg')
result = model(image)
print(result['text'])
What makes GOT OCR Transformers better than other OCR tools?
GOT OCR Transformers leverages the power of Transformer models, which are known for their superior performance in sequence-to-sequence tasks. This results in better accuracy and handling of complex text layouts.
Can I use GOT OCR Transformers for commercial projects?
Yes, GOT OCR Transformers is open source and can be used for both research and commercial purposes. However, ensure compliance with the licensing terms.
How do I improve the accuracy of text extraction?
To improve accuracy, ensure high-quality input images, preprocess images to enhance visibility, and experiment with different models or fine-tuning for specific use cases.