Interactive AI Learning System
Esharati | إشارتي
Esharati | إشارتي
Saudi Sign Language System
Project Overview
Esharati is a machine learning-based system designed to help users learn and recognize Saudi Sign Language (SSL). Developed using Python and KivyMD, the system employs a Random Forest model to accurately identify and interpret SSL gestures. Users can learn the language through categorized lessons, take tests to assess their progress. The app aims to bridge communication gaps and increase social inclusion for the deaf community in Saudi Arabia.
- Developed a mobile application using Python and KivyMD, enabling users to learn Saudi Sign Language interactively.
- Built and trained machine learning models (Random Forest, SVM) for gesture recognition, with Random Forest achieving 100% accuracy.
- Created and structured a dataset by adapting Arabic Sign Language (ArSL) data due to the lack of Saudi-specific datasets, covering 28 alphabet and 10 number classes with over 48K images.
- Integrated the trained model into the mobile application, providing real-time gesture recognition for users.
Core Competencies & Skills
Programming Languages
Python
Mobile Development
Kivy, KivyMD
Machine Learning
Scikit-learn, Random Forest, SVM
Computer Vision
Image Processing, Gesture Recognition
Data Science
Dataset Curation, Preprocessing, Annotation
AI Implementation
Real-time Integration, Optimization
Software Design
UI/UX Design for Mobile
Project Demo
Project Poster
Visual summary of the project goals, methodology, and results.
Research Paper
Deep dive into the technical implementation and research findings.