from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

# Load model
processor = TrOCRProcessor.from_pretrained("Piyush3142/trocr-sanskrit-ocr")
model = VisionEncoderDecoderModel.from_pretrained("Piyush3142/trocr-sanskrit-ocr")

# OCR inference
image = Image.open("sanskrit_manuscript.jpg").convert("RGB")
pixel_values = processor(image, return_tensors="pt").pixel_values
outputs = model.generate(pixel_values, max_length=256)
text = processor.batch_decode(outputs, skip_special_tokens=True)[0]

print(text)  # IAST output

Convert IAST to Devanagari

python

from indic_transliteration.sanscript import transliterate, IAST, DEVANAGARI

devanagari = transliterate(text, IAST, DEVANAGARI)
print(devanagari)

Training Details

Parameter	Value
Dataset	yzk/veda-ocr-ms
Train samples	10,560
Test samples	1,174
Epochs	2
Batch size	4
Learning rate	2e-5
CER	~68%
WER	~80%

Limitations

Trained on printed Vedic manuscripts
Best performance on similar style texts
May struggle with handwritten or degraded images

License

MIT