Sign Language Detection

This repository contains my project on Sign Language Detection using two different approaches:

1. CNN-Based Approach
2. Mediapipe + Random Forest Approach

Table of Contents

• Introduction
• Approach 1: CNN
• Approach 2: Mediapipe + Random Forest

Introduction

Sign language detection is a critical application in making communication more inclusive. This project explores two distinct methods to recognize hand gestures and detect alphabets.

Approach 1: CNN

In this approach, I trained a Convolutional Neural Network (CNN) on preprocessed sign language images.

Key Steps:

• Gaussian Blur Filters: To minimize background influence.
• Data Augmentation: Techniques like zooming, flipping, and rotation.
• Histogram Equalization: To enhance image contrast.

Example Outputs:

Prediction1:	Prediction2:	Prediction3:

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Approach 2: Mediapipe + Random Forest

This approach uses Mediapipe to extract hand landmarks and a Random Forest classifier for gesture recognition.

Key Steps:

• Landmark Extraction: Using Mediapipe to extract hand and finger positions.
• Feature Storage: Saving the landmarks for classification.
• Random Forest Classifier: Training the model on the extracted features.

Example Outputs:

Prediction1:	Prediction2:	Prediction3:

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