In the rapidly evolving world of OCR (Optical Character Recognition), the launch of OCR-2.0 has paved the way for advanced functionalities and a unified end-to-end model. This guide will walk you through the steps to utilize this model using the Hugging Face Transformers library.
Getting Started with OCR-2.0
- Requirements: Ensure you have the following Python packages:
- torch==2.0.1
- torchvision==0.15.2
- transformers==4.37.2
- tiktoken==0.6.0
- verovio==4.3.1
- accelerate==0.28.0
- Environment: This guide is tested on Python 3.10.
Loading the Model
Once you have your environment set up, you can load the OCR-2.0 model with the following code:
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("srimanth-dGOT_CPU", trust_remote_code=True)
model = AutoModel.from_pretrained("srimanth-dGOT_CPU", trust_remote_code=True, low_cpu_mem_usage=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
model = model.eval()This code snippet is akin to opening a toolbox before starting a DIY project. Just as you want to have the right tools ready at hand, you must load the correct model and tokenizer for effective results.
Using the Model for OCR
Next, you need to input your test image and specify the OCR type. The command can be tailored based on the desired output format:
# Input your test image
image_file = "xxx.jpg"
# Plain text OCR
res = model.chat(tokenizer, image_file, ocr_type="ocr")
# Format texts OCR
# res = model.chat(tokenizer, image_file, ocr_type="format")
# Fine-grained OCR
# res = model.chat(tokenizer, image_file, ocr_type="ocr", ocr_box=....)
# More OCR types can be specified similarly.Rendering OCR Results
To visualize your results effectively, you can render formatted OCR outputs. This is similar to pruning your garden after planting: it ensures that your work is presented neatly:
# Render the formatted OCR results
# res = model.chat(tokenizer, image_file, ocr_type="format", render=True, save_render_file=".demo.html")
print(res)Troubleshooting Tips
While this setup should work smoothly, you might encounter some issues along the way. Here are some troubleshooting tips to assist you:
- Common Errors: Check for any installation or import errors. Ensure that all dependencies are installed correctly.
- Model Loading Issues: Verify that the model name is correct and that you’re connected to the internet.
- Image File Issues: Ensure that the image path is correct and that the image file is accessible.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Happy coding!
