Bridging the Communication Gap: Advanced ASL Alphabet Recognition Using VGG16

Image classification may improve deaf-non-signer communication by recognizing the American Sign Language (ASL) alphabet. Deep Learning (DL) applications, especially the VGG16 architecture, are being studied to accurately classify ASL hand signals. The dataset includes images of the 26 English alphabet letters and motions for “space,” “delete,” and “nothing.” To overcome illumination, hand orientation, and background noise fluctuation, significant preprocessing, including normalization and augmentation, improved model performance. VGG16 architecture, trained on ImageNet, was used in this work. Later, the model was modified to detect ASL signals more accurately for a given activity. The model distinguished comparable hand motions with minimal errors. In this investigation, the VGG16 framework was used to overcome some of the constraints of typical Convolution Neural Networks (CNNs), including processing complicated patterns and reaching high accuracy. With its deeper and more polished design, VGG16 increases classification and ASL sign recognition. DL showed great potential in enhancing ASL recognition technology, creating a dependable and versatile solution that can be integrated into numerous assistive devices and apps. This effort bridges the linguistic distinction between ASL end-users and laypeople by offering a reliable ASL alphabet recognition mechanism, promoting acceptance and clarity.