🏆 TrOCR Fine-Tuned Model (Handwritten Text Recognition)

📌 Model Overview

This is a fine-tuned Microsoft TrOCR Large model for handwritten text recognition. It has been trained on a dataset containing scanned handwritten documents.

• Base Model: Microsoft TrOCR Large
• Fine-tuned On: IAM Handwritten Dataset
• Use Case: Extract text from scanned handwritten documents
• Framework: PyTorch + Transformers (Hugging Face)
• Large File Support: Uses git-lfs for model files

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🚀 How to Use This Model

You can load and use the fine-tuned model with transformers in Python as follows:

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

# Load model and processor
processor = TrOCRProcessor.from_pretrained("Gitesh2003/TrOCR")
model = VisionEncoderDecoderModel.from_pretrained("Gitesh2003/TrOCR")

# Load an image
image = Image.open("handwritten_sample.jpg").convert("RGB")

# Process and predict text
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print("Extracted Text:", extracted_text)