ASL-3DCNN: American sign language recognition technique using 3-D convolutional neural networks

• Published in: Multimedia tools and applications Vol. 80; no. 17; pp. 26319 - 26331
• Main Authors: Sharma, Shikhar, Kumar, Krishan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Computing time; Data Structures and Information Theory; Datasets; Model testing; Multimedia Information Systems; Neural networks; Pattern recognition; Sign language; Special Purpose and Application-Based Systems; Video data; 3D CNN; Speech; ASL; Deep Learning
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-10768-5

The communication between a person from the impaired community with a person who does not understand sign language could be a tedious task. Sign language is the art of conveying messages using hand gestures. Recognition of dynamic hand gestures in American Sign Language (ASL) became a very important challenge that is still unresolved. In order to resolve the challenges of dynamic ASL recognition, a more advanced successor of the Convolutional Neural Networks (CNNs) called 3-D CNNs is employed, which can recognize the patterns in volumetric data like videos. The CNN is trained for classification of 100 words on Boston ASL (Lexicon Video Dataset) LVD dataset with more than 3300 English words signed by 6 different signers. 70% of the dataset is used for Training while the remaining 30% dataset is used for testing the model. The proposed work outperforms the existing state-of-art models in terms of precision (3.7%), recall (4.3%), and f-measure (3.9%). The computing time (0.19 seconds per frame) of the proposed work shows that the proposal may be used in real-time applications.