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Arabic Sign language Recognition and Translation

Team Members

  • Seif Sherif Assad Ali
  • Sama Ahmed ElSayed
  • Yusuf Sobhy Sadek Elmeligy
  • George Nemr Mellek Poqtor
  • Omar Elsayed Elsayed Mousa

Project Overview

This project focuses on developing a Hand Gesture Recognition System that translates Arabic Sign Language (ArSL) into text prompts using deep learning and computer vision. The goal is to create an accessible tool for individuals who rely on sign language for communication, enhancing inclusivity and bridging the gap between the deaf and hearing communities.
Link to Project Presentation: https://www.canva.com/design/DAGnKgSTjkU/oMdTk6_GvWX8X72XdJCY4Q/view?utm_content=DAGnKgSTjkU&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=h176d3d2eb1

Motivation

Millions of people use Arabic Sign Language (ArSL) as their primary means of communication, yet there is a significant gap in accessibility tools tailored to the Arabic-speaking deaf community. This project aims to leverage AI and machine learning to provide a practical solution that can recognize hand gestures and convert them into text in real time.

Project Scope & Features

  • Dataset: RGB images of Arabic Sign Language letters.
  • Preprocessing: Image cleaning, resizing, augmentation, and normalization.
  • Model: Using Vision Transformers after trying different models. (Such as Convolutional Neural Networks (CNNs) for hand gesture classification)
  • Output: Conversion of gestures into Arabic text.
  • Future Expansion: Potential integration with speech synthesis for text-to-speech conversion.

Project Goals

✔ Develop a robust gesture recognition model trained on Arabic sign language datasets.
✔ Ensure high accuracy in gesture-to-text conversion.
✔ Implement a user-friendly interface for real-time interaction.
✔ Promote accessibility and inclusivity for the deaf and hard-of-hearing community.

Extracted Dataset

Deep Learning Models

https://drive.google.com/drive/folders/14_aZ9G3FArZuQPstq-lvlq-59tF3Immg?usp=sharing

About

This is an integrated software with a CNN model that predicts sign language with accuracy 98%

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