---
library_name: transformers
language:
- ne
metrics:
- cer
pipeline_tag: image-to-text
---
## Devanagari OCR with TrOCR

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.

### Model Details

- **Model:** `syubraj/TrOCR_Nepali`
- **Processor:** TrOCRProcessor combining a Vision Transformer (ViT) feature extractor and a tokenizer.

### How to Use

You can use this model in Python with the following steps:

```python
from transformers import VisionEncoderDecoderModel, TrOCRProcessor, AutoTokenizer
from PIL import Image
import torch

# Load the model and processor
tokenizer = AutoTokenizer.from_pretrained("syubraj/TrOCR_Nepali")
model = VisionEncoderDecoderModel.from_pretrained("syubraj/TrOCR_Nepali")
processor = TrOCRProcessor.from_pretrained("syubraj/TrOCR_Nepali")

# Load image
image = Image.open("path_to_image").convert("RGB")

# Preprocess image
pixel_values = processor(image, return_tensors="pt").pixel_values

# Generate text
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)
```