A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture

Abstract

The domain of hand sign recognition, an integral facet of computer vision, encompasses a wide array of practical applications, ranging from interpreting sign language and recognizing gestures to facilitating human-computer interaction. This research elucidates the introduction of a Convolutional Neural Network (CNN) model tailored to the identification of hand signs representing the English alphabet. For model training and validation, a dataset comprising 26,000 grayscale images of hand signs was employed. The model architecture embraced a profound CNN design, featuring numerous layers for convolution and pooling, followed by fully connected layers. Employing the Adam optimizer, the training procedure yielded an impressive accuracy of 96.7% when evaluated on the Kaggle dataset. These outcomes underscore the effectiveness of the proposed CNN model in precisely discerning hand signs corresponding to the English alphabet. The model's potential utility extends to the recognition of intricate manual gestures and realtime applications, including aiding individuals with motor impairments and enriching virtual reality experiences. Hence, this study accentuates the capacity of deep learning to propel the domain of hand sign recognition forward.