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TrOCR_Nepali

Image-to-Text
Transformers
Safetensors
Nepali
vision-encoder-decoder
image-text-to-text
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metadata
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:

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)