Iranian Sign Language Recognition System (ISLRS)

This project focuses on the real-time recognition of Iranian Sign Language (ISL) using computer vision and machine learning techniques. It aims to bridge communication gaps for deaf individuals in Iran by enabling automatic translation of hand gestures into readable or spoken language.

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Project Overview

The goal is to build a system capable of recognizing Iranian sign language letters from live webcam input. The solution uses image processing and a trained machine learning model to classify gestures into 37 sign language classes, corresponding to Persian alphabet letters.

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Project Components

The implementation is modular and consists of the following stages:

1. Collect Images
   Captures images of sign gestures for each class using a webcam and stores them in structured folders.

2. Save Landmarks
   Uses MediaPipe to detect and store hand landmarks from the collected images.

3. Create Dataset
   Extracts features (x, y coordinates of 21 hand landmarks) and stores them in a .csv file with labels.

4. Train Network
   Trains a Random Forest classifier using the dataset for gesture recognition.

5. Main Application
   Loads the trained model and performs real-time gesture recognition via webcam.

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Technologies Used

• Python
• OpenCV – for image capture and processing.
• MediaPipe – for hand tracking and landmark extraction.
• scikit-learn – for machine learning model (Random Forest).
• NumPy / Pandas – for data manipulation.

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