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

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.

Automated summary

Communication between those who understand sign language and those who do not is challenging. To address the challenge of dynamic ASL recognition, 3-D CNNs were employed, with training and testing on a dataset showing improved precision, recall, and f-measure compared to existing models. The proposed approach also shows potential for real-time applications with a computing time of 0.19 seconds per frame.