---
tags:
- trocr
- ocr
- text-recognition
- pytorch
- fine-tuned
license: mit
---

# 🏆 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

---

## 🚀 **How to Use This Model**
You can load and use the fine-tuned model with `transformers` in Python as follows:

```python
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)