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Indian Sign Language Recognition: A Review of CNN-Based Approaches and Challenges

Authors:

¹ Prajakta Dawange, ² Omkar Pattnaik, ³Neha Ranjan, ⁴Shalini Mhaske, ⁵Gitanjali Khatik

²Associate Professor, School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra-422213, India.

Mail ID: omkar.pattnaik@sandipuniversity.edu.in

^1,3,4,5 Student, School of Computer Science and Engineering, Sandip University, Nashik, Maharashtra-422213, India.

prajdawange2001@gmail.com, ranjanneha536@gmail.com, shalinimhaske26199@gmail.com, gitanjalikhatik@gmail.com, 

*Corresponding authors: prajdawange2001@gmail.com, omkar.pattnaik@sandipuniversity.edu.in

Abstract- Communicating with the person having hearing failure is ceaselessly a major challenge. The work shown in paper is an exertion(extension) towards looking at the challenges in classification of characters in Indian Sign Language (ISL). Sign Dialect is not adequate for communication of people with hearing capacity or people with talk inadequacy. The signals made by the people with inadequacy gets mixed or disarranged for some person who has never learnt this lingo. Communication needs to be in both ways. In this paper, we display a Sign Dialect affirmation utilizing Indian Sign Lingo. The client must be able to capture pictures of hand movements utilizing a web camera in this examination, and the system must anticipate and show up the title of the captured picture. The captured picture encounters the course of action of planning steps which consolidate distinctive Computer vision strategies such as the change to gray-scale, extending and cover operation. Convolutional Neural arrange (CNN) is utilized to plan our show and recognize the pictures. Our appear has finished precision around 95%.

Keyword - Convolutional Neural Network (CNN), Hand Gesture, Hearing-impaired people, Sign Language, Sign Language Recognition (SLR).