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.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI) |
| Publisher | IEEE |
| Pages | 000129-000134 |
| ISBN (Print) | 9798350353433 |
| DOIs | |
| Publication status | Published - 19 Nov 2024 |
Keywords
- AI accessibility
- American Sign Language (ASL)
- CRISP-DM
- Deaf and hard of hearing
- Fingerspelling recognition
- Levenshtein distance
- Machine learning
- MediaPipe
- PyTorch
- Transformer models
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