An AI-Based Approach To American Sign Language Alphabet Recognition
DOI:
https://doi.org/10.58445/rars.3202Keywords:
Artificial Intelligence (AI), Image Recognition, Computer Vision, American Sign Language, Alphabet RecognitionAbstract
This research presents the development of an AI-powered image recognition model designed to translate American Sign Language (ASL) alphabet gestures into corresponding text characters (A-Z). The models utilize machine learning algorithms, specifically random forest and multi-layer perceptron (MLP) Classifiers with logistic regression as a baseline, trained on a dataset of labelled hand gesture images to classify static signs representing individual letters accurately. Both models achieve high accuracy in identifying hand gestures under controlled lighting and background conditions in a short time frame, with the MLP achieving an accuracy of 98.79% and the random forest achieving a slightly higher accuracy of 99.38% while the baseline achieved an accuracy of 97.66%. These models are particularly beneficial for individuals who are hearing impaired or for those who wish to improve communication with ASL users. Future improvements may include expanding to dynamic gestures as well as recognizing full words or phrases.
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