MACHINE LEARNING ALGORITHMS FOR ASL IMAGE RECOGNITION WITH LENET5 FEATURE EXTRACTION

Vandana Killari, Ratan Kumar Sajja, Satya Avinash Nimmakayala, Varun Ippili

Abstract


American Sign Language is used by mute and deaf people so that they can interact with the people around them. It is used by approximately 2,50,000-5,00,000 Americans (and some Canadians) of all ages. Over the period of time, many have proposed different methods for recognition of ASL. Sign language Recognition is a complex technical problem due to the difficulty of visual analysis of hand motions and the highly structured nature of sign language. Hence the accuracy is not achieved. To enhance this accuracy, the proposed system compares different machine learning classification algorithms using Lenet5 architecture for feature extraction. Lenet5 is one of the architectures of Convolution Neural Network (CNN). The proposed system uses machine learning algorithms like Neural Network, Decision Tree Classifier, K-Nearest Neighbour and Support Vector Machine. There is a raise in accuracy using Neural Network algorithm when compared to other machine learning algorithms. The proposed solution was tested on data samples from ASL data sets and achieved an overall accuracy of 99.99% using Neural Network.


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DOI: https://doi.org/10.26483/ijarcs.v13i3.6828

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