Image-to-Text
Transformers
PyTorch
textract
feature-extraction
ocr
vision-language
qwen2-vl
custom-model
text-extraction
document-ai
high-accuracy
custom_code
Instructions to use BabaK07/textract-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BabaK07/textract-ai with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="BabaK07/textract-ai", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BabaK07/textract-ai", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 617 Bytes
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"""
Basic usage example for the Custom OCR Model.
"""
from transformers import AutoModel
from PIL import Image
def basic_ocr_example():
"""Basic OCR usage example."""
# Load model
model = AutoModel.from_pretrained("your-username/your-model-name", trust_remote_code=True)
# Load image
image = Image.open("document.jpg")
# Extract text
result = model.generate_ocr_text(image, use_native=True)
print(f"Extracted text: {result['text']}")
print(f"Confidence: {result['confidence']:.3f}")
return result
if __name__ == "__main__":
basic_ocr_example()
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