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Abstract

Sign language is the primary medium of communication for deaf and dumb individuals, but it is difficult to interpret for every demographic, which makes communication extremely difficult. Bangla is among the most widely spoken languages worldwide, and substantial research on Bangla Sign Language (BdSL) has emerged to address this issue. In recent years, researchers have been working to automate BdSL recognition using different techniques. This review paper evaluates research trends in BdSL by comparing the features and evaluation outcomes of various systems and approaches applied to both existing and novel datasets. We have gathered and integrated metadata from datasets encompassing all BdSL alphabets and numbers implemented to date. The analysis of this paper shows that most suggested models work well on images with static and single-handed signs, but performance drops in complicated backgrounds. Additionally, we concentrated on identifying insights and parallels within the existing systems, identifying research gaps, and suggesting potential future directions.

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