Indian Sign Language Recognition using transfer learning with Efficient Net

Abstract

ABSTRACT Automatic conversion of sign language into text or speech can be really useful in communicating among deaf or mute people with no sign language knowledge. The present need is to create an automated system that can translate ISL signals into regular text and vice versa. It is advantageous for both groups to communicate their needs to one another while using publicly accessible services, such as banking, traffic signaling, and ticketing. In this manuscript, Indian Sign Language Recognition using Transfer Learning with Efficient Net (ISLR-EfficientNetB0) is proposed for Indian sign language recognition. Initially, the input images are gathered via the Indian Sign Language (ISL) dataset. The input image is pre-processing by Fairness-aware Collaborative Filtering (FACF) method. In pre-processing stage noise are removed from input images and image resizing is done. Then, Efficient Net B0 is employed to identify ISL. The proposed model is implemented on Python. Then the efficiency of the proposed approach is compared with existing models. The accuracy, precision, specificity, sensitivity, F1-score, computational time, error rate and RoC of the suggested approaches are all assessed. The proposed ISLR-EfficientNetB0 method attains 31.42%, 26.04%, and 20.45% higher accuracy, 19.24%, 26.35%, and 24.02% higher precision, 15.84%, 18.91%, and 28.16% higher specificity and 17.06%, 21.36%, and 32.47% higher sensitivity compared with existing methods, such as Indian sign language recognition under machine learning methods (ISLR-KNN), ISL recognition scheme utilizing SURF with SVM and CNN (ISLR-SVM-CNN) and Benchmarking deep neural network methods for ISL recognition (ISLR-VGG16) respectively.