# Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech

## TL;DR

This paper presents a deep learning-based vision system that recognizes hand sign digits and converts them into spoken Bangla, aiding communication for speech-impaired individuals with high accuracy.

## Contribution

The work introduces a CNN-based system that accurately classifies hand sign digits in Bangla and converts them into speech, extending gesture recognition to the Bangla language for the first time.

## Key findings

- Achieved 92% accuracy in hand sign digit classification
- Developed a web application demonstrating the system
- Provides an assistive tool for speech-impaired Bangla speakers

## Abstract

Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at http://bit.ly/signdigits2banglaspeech.