---
license: apache-2.0
tags:
- trocr
- math-ocr
- handwritten-math
- latex
- computer-vision
- image-to-text
datasets:
- custom-math-dataset
---

# TrOCR Fine-tuned for Mathematical Expressions

This model is a fine-tuned version of [fhswf/TrOCR_Math_handwritten](https://huggingface.co/fhswf/TrOCR_Math_handwritten) for recognizing handwritten mathematical expressions and converting them to LaTeX format.

## Model Description

- **Architecture**: VisionEncoderDecoder (ViT + Transformer)
- **Base Model**: fhswf/TrOCR_Math_handwritten
- **Training Data**: Custom mathematical expressions dataset
- **Purpose**: Convert images of mathematical equations to LaTeX code

## Intended Uses

- Digitizing handwritten math equations
- Educational applications
- Scientific document processing
- Math notation recognition

## Usage

```python
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

processor = TrOCRProcessor.from_pretrained("Ntsako12/TrOCR_Tuned")
model = VisionEncoderDecoderModel.from_pretrained("Ntsako12/TrOCR_Tuned")

image = Image.open("math_equation.jpg").convert("RGB")
pixel_values = processor(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)  # Output: rac{1}{2} + rac{3}{4}

Training
Epochs: 10

Batch Size: 16

Learning Rate: 5e-5

Framework: PyTorch with Hugging Face Transformers

Limitations
Performance may vary with different handwriting styles

Complex nested expressions might be challenging

Requires clear, well-written mathematical expressions