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Advances in AI for Automatic Sign Language Recognition: A Comparative Study of Machine Learning Approaches

D. Manindra Varma*, Ch. Sekhar**

*-** GMR Institute of Technology, Rajam, Andhra Pradesh, India.

Periodicity:October - December'2024

Abstract

Sign language recognition is a vital field within artificial intelligence, aiming to bridge communication gaps between deaf or hard-of-hearing communities and others by translating visual gestures into text or speech. Automatic Sign Language Recognition (ASLR) systems seek to interpret these complex and nuanced gestures with greater accuracy, expanding access to information communicated through sign language. This paper presents a comparative review of machine learning methods used in ASLR, emphasizing their impact on improving communication for hearing-impaired individuals. It also explores the primary challenges in ASLR, such as variability in signs and the complexities of gesture recognition. Advanced feature extraction techniques like SIFT, HOG, and SURF are examined for their role in enhancing ASLR system performance. Additionally, a bibliometric study highlights significant trends and advancements in intelligent systems for sign language recognition over the past two decades. This paper synthesizes recent research on ASLR technologies, supporting the development of more effective communication tools and fostering social inclusivity for deaf and hard-of-hearing communities.

Keywords

Sign Language Recognition (SLR), Automatic Sign Language Recognition (ASLR), Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradient (HOG), Speeded Up Robust Features (SURF).

How to Cite this Article?

Varma, D. M., and Sekhar, Ch. (2024). Advances in AI for Automatic Sign Language Recognition: A Comparative Study of Machine Learning Approaches. i-manager’s Journal on Software Engineering, 19(2), 37-45.

References

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