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--- |
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library_name: transformers |
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language: |
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- ne |
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metrics: |
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- cer |
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pipeline_tag: image-to-text |
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--- |
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## Devanagari OCR with TrOCR |
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This model is a Devanagari Optical Character Recognition (OCR) model based on VisionEncoderDecoder architecture, fine-tuned on Nepali/Devanagari script. The model uses the `TrOCRProcessor` from Hugging Face to process and generate text from images. |
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### Model Details |
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- **Model:** `syubraj/TrOCR_Nepali` |
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- **Processor:** TrOCRProcessor combining a Vision Transformer (ViT) feature extractor and a tokenizer. |
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### How to Use |
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You can use this model in Python with the following steps: |
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```python |
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from transformers import VisionEncoderDecoderModel, TrOCRProcessor, AutoTokenizer |
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from PIL import Image |
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import torch |
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# Load the model and processor |
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tokenizer = AutoTokenizer.from_pretrained("syubraj/TrOCR_Nepali") |
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model = VisionEncoderDecoderModel.from_pretrained("syubraj/TrOCR_Nepali") |
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processor = TrOCRProcessor.from_pretrained("syubraj/TrOCR_Nepali") |
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# Load image |
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image = Image.open("path_to_image").convert("RGB") |
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# Preprocess image |
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pixel_values = processor(image, return_tensors="pt").pixel_values |
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# Generate text |
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generated_ids = model.generate(pixel_values) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(generated_text) |
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``` |
