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umangchaudhari/
gujarati-ocr

Name: gujarati-ocr
Availability: InStock

safetensors

vision-encoder-decoder

ocr

gujarati

document-digitization

apache-2.0

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Gujarati OCR Model

An Optical Character Recognition model for the Gujarati language, developed for digitizing printed and scanned Gujarati documents.

Developed by

GIL AI Department

Model Description

This model is trained to extract Gujarati text from scanned document images, printed text images, and digital document pages. It handles a wide range of Gujarati script characters including conjuncts, matras, and diacritics.

Performance

Exact Word Accuracy: 96.2%
Average Character Error Rate (CER): 1.35%
Test set: 10,090 real scanned Gujarati word images

Intended Use

Digitization of printed Gujarati government documents
Scanned book and newspaper text extraction
Automated Gujarati document processing pipelines

Training Data

Trained on real scanned Gujarati printed document images with verified ground truth labels.

How to Use

python

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

processor = TrOCRProcessor.from_pretrained("umangchaudhari/gujarati-ocr")
model = VisionEncoderDecoderModel.from_pretrained("umangchaudhari/gujarati-ocr")

image = Image.open("your_gujarati_document.jpg").convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values

generated = model.generate(pixel_values, max_new_tokens=64)
text = processor.batch_decode(generated, skip_special_tokens=True)[0]
print(text)