Sign Language Recognition Using Machine Learning

G Manoj Kumar 1, Kandisa Siva 2

1Assistant Professor, 2 MCA Final Semester, Master of

Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

sivakandisa112@gmail.com

ABSTRACT:

Sign language recognition using Machine Learning (ML) aims to bridge the communication gap between deaf and hearing individuals. The system recognizes hand gestures and translates them into text or speech. It uses image processing and ML algorithms to identify different sign patterns accurately. Cameras capture gesture images, which are preprocessed and converted into useful features. A trained ML model classifies the gestures into corresponding words or alphabets. The system improves communication efficiency in real-time applications. It reduces dependency on human interpreters and enhances accessibility. Sign language recognition can be applied in education, healthcare, and public services. Continuous improvements in ML increase the accuracy and speed of recognition. This technology promotes inclusive communication and supports equal opportunities for people with hearing and speech impairments. Additionally, deep learning techniques such as Convolutional Neural Networks (CNNs) further enhance gesture recognition by learning complex visual patterns from large datasets. The system can operate in real time, providing instant feedback and seamless interaction between users. It is capable of recognizing both static hand signs and dynamic gesture sequences with high precision. Integration with speech synthesis and mobile applications makes the technology more practical and accessible in everyday life. As research continues to advance, sign language recognition systems are becoming more reliable, efficient, and widely adopted, contributing to a more inclusive and accessible society.

Keywords: Sign Language Recognition, Machine Learning, Deep Learning, Computer Vision, Image Processing, Hand Gesture Recognition, Convolutional Neural Networks (CNN), Real-Time Gesture Detection, Speech Synthesis, Accessibility, Human-Computer Interaction, Inclusive Communication.