Model Name: TrOCR Fine-Tuned on Custom Dataset

This model is a fine-tuned version of Microsoft's TrOCR on a custom dataset for handwritten text extraction from scanned documents.

🧠 Model Architecture

Base model: Microsoft TrOCR (base)
Used with: CRAFT for text detection
Fine-tuned with: OCR-specific dataset

📁 Files in this repository:

pytorch_model.bin: Model weights (2.1 GB)
config.json, tokenizer_config.json, etc.
Training and evaluation scripts (optional)

🚀 How to Use

from transformers import VisionEncoderDecoderModel, TrOCRProcessor
from PIL import Image
import torch

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

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

# OCR
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)