Version 1
: Received: 25 December 2022 / Approved: 26 December 2022 / Online: 26 December 2022 (04:10:07 CET)
How to cite:
Anderson, J.; Casad, R.; Cathcart, C.; Nguyen, A.; Khan Mohd, T. Median American Sign Language Interpretation Software. Preprints2022, 2022120471. https://doi.org/10.20944/preprints202212.0471.v1
Anderson, J.; Casad, R.; Cathcart, C.; Nguyen, A.; Khan Mohd, T. Median American Sign Language Interpretation Software. Preprints 2022, 2022120471. https://doi.org/10.20944/preprints202212.0471.v1
Anderson, J.; Casad, R.; Cathcart, C.; Nguyen, A.; Khan Mohd, T. Median American Sign Language Interpretation Software. Preprints2022, 2022120471. https://doi.org/10.20944/preprints202212.0471.v1
APA Style
Anderson, J., Casad, R., Cathcart, C., Nguyen, A., & Khan Mohd, T. (2022). Median American Sign Language Interpretation Software. Preprints. https://doi.org/10.20944/preprints202212.0471.v1
Chicago/Turabian Style
Anderson, J., Anh Nguyen and Tauheed Khan Mohd. 2022 "Median American Sign Language Interpretation Software" Preprints. https://doi.org/10.20944/preprints202212.0471.v1
Abstract
The Median American Sign Language Interpretation Software (ASL) Interpretation Software is a web application that is capable of interpreting American Sign Language in real-time, utilizing an internet connection and a primary web camera, complete with basic phrases and letters. Extensive use of Deep Learning and Neural Networks, specifically Convoluted Neural Networks, enables Median to interpret video inputs and generate accurate results directly displayed to the user in text format. The ultimate goal for Median is to have it act as a bridge between hearing people and members of the deaf community, allowing deaf people to communicate with non-signing people using American Sign Language. Furthermore, Median has been designed to benefit people who lack access to a human ASL Translator, as its format as a website allows it to be accessible anywhere at any time, giving increased availability over human interpreters. Median is designed to be a very versatile program with great potential for growth and expansion.
Keywords
Deep learning; Convoluted Neural Networks; LSTM; MediaPipe; Google Cloud; Object detection; Classification
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
