Development of an Indian Sign Language Detection System for Deaf and Mute Communities: A Machine Lea

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 05 | May 2025

p-ISSN: 2395-0072

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Development of an Indian Sign Language Detection System for Deaf and Mute Communities: A Machine Learning Approach Vishakha Mishra#1, Divya Dron*2, Surabhi Soni#3, Sanjana Tiwari#4 ,Prof. Priyanka Devi #5 1BTECH,Student,Dept. of Information technology , Govt. Engineering College, Bilaspur, Chhattisgarh,INDIA 2BTECH,Student,Dept. of Information technology , Govt. Engineering College, Bilaspur, Chhattisgarh,INDIA

3BTECH,Student,Dept. of Information technology , Govt. Engineering College, Bilaspur, Chhattisgarh,INDIA 4BTECH,Student,Dept. of Information technology , Govt. Engineering College, Bilaspur, Chhattisgarh,INDIA

5Assistant Professor, Dept. of Information technology , Govt. Engineering College, Bilaspur, Chhattisgarh,INDIA

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract— This project focuses on the development of a real-time Indian Sign Language (ISL) Detection System using machine learning techniques, specifically Convolutional Neural Networks (CNNs) and OpenCV. The system is designed to bridge the communication gap between individuals who use ISL and those who do not, by recognizing hand gestures and converting them into text or speech. Given that over half of the deaf population in India relies on ISL as their primary means of communication, this project addresses the significant barrier that non-signers face in understanding sign language. The system operates by detecting and tracking hand gestures using OpenCV’s real-time computer vision capabilities. Features from the hand gestures are extracted and classified using a trained CNN model, which interprets the gestures into corresponding ISL symbols. These recognized gestures are then converted into text or speech output, enabling non- sign language users to understand the communication. The primary objective of this project is to ensure the system performs in real-time, providing high accuracy and usability for various real-world applications. Additionally, the project emphasizes the need for a simple and intuitive interface to facilitate smooth communication. This solution aims to enhance inclusivity for the hearingimpaired community in India by offering a robust and accessible tool for effective communication.

Keywords— Convolutional Neural Network (CNN), OpenCV, Hand gestures are converted into text or speech, Realtime performance

1. INTRODUCTION Sign language serves as the primary communication system for many individuals with hearing impairments. However, the lack of widespread understanding of sign language frequently leads to communication barriers between signers and nonsigners [2][5]. In recent years, advancements in computer vision and machine learning have opened new avenues for bridging this gap [2][4]. This paper explores the development of a real-time sign language detection system that utilizes machine learning, particularly CNNs, to detect and classify hand gestures [3][4]. By converting these gestures into text or speech, the system aims to facilitate seamless communication between individuals who use sign language and those who do not. The main motivation behind this work is to enhance accessibility and inclusivity for the hearing-impaired community by providing a tool that can interpret sign language in real-time [5][6][7]. 1.1

LITERATURE REVIEW

The development of sign language detection systems has gained attention in the fields of computer vision and machine learning, with various approaches being explored to bridge the communication gap between sign language users and nonsigners [4][7][8]. Below is an overview of key studies and advancements in this area: 1.1.1 Gesture Recognition Systems

Many early systems relied on traditional image processing techniques such as skin color detection, edge detection, and template matching to identify hand gestures [2][9]. For instance, static images of hand shapes were used for feature extraction and classification. However, these methods were limited by variability in lighting, background noise, and hand shapes, often resulting in reduced accuracy under uncontrolled conditions [2][3][9].

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