American Sign Language Character Recognition using Convolutional Neural Networks
Atesam Abdullah, Nisar Ali, Raja Hashim Ali, Zain ul Abideen, Ali Zeeshan Ijaz, Abdul Bais
Abstract
This study presents a convolutional neural network (CNN) architecture developed using the TensorFlow framework to accurately recognize individual letters of American Sign Language (ASL). The CNN architecture consists of various layers including two-dimensional convolutional layers, max-pooling layers, batch normalization layers, dropout layers, and fully connected layers. The model achieved a mean validation accuracy of 95.48% and a test accuracy of 99.77% in identifying ASL characters. However, the live visual depictions revealed certain difficulties encountered by the model in identifying some ASL letters, highlighting the need for further improvement of the model’s framework and dataset curation. This research contributes to the scholarly discussion on the use of machine learning approaches in identifying sign language alphabets and provides insights into the feasibility and effectiveness of utilizing these techniques in ASL recognition tasks.