Utilising transformers for American Sign Language fingerspelling recognition

Book chapter

Pinnington, J., Souag, A. and Azhar, H. 2024. Utilising transformers for American Sign Language fingerspelling recognition. in: 24th International Symposium on Computational Intelligence and Informatics (CINTI 2024)
AuthorsPinnington, J., Souag, A. and Azhar, H.
Abstract

This paper presents a machine learning approach to American Sign Language (ASL) fingerspelling recognition using Transformer models. Addressing the challenges of high variability in hand shapes, movement, and signing speed, the study utilises the new ASL Fingerspelling Recognition Corpus and the CRISP-DM methodology to develop and evaluate a proof-of-concept model. The model achieved a mean Levenshtein distance of 4.7, corresponding to an error rate of 16.37%. This research demonstrates the feasibility of using advanced AI techniques to enhance accessibility for the deaf and hard of hearing communities by translating ASL fingerspelling into text.

KeywordsAmerican Sign Language (ASL); Fingerspelling recognition; Transformer models; Machine learning; CRISP-DM; AI accessibility; Deaf and hard of hearing; Levenshtein distance; PyTorch; MediaPipe
Year2024
Book title24th International Symposium on Computational Intelligence and Informatics (CINTI 2024)
Output statusIn press
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Deposited27 Nov 2024
Related URLhttps://conf.uni-obuda.hu/cinti2024/
EventIEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI 2024)
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