Open AccessHuman‐like sign‐language learning method using deep learning
Author(s) -
Ji Yangho,
Kim Sunmok,
Kim YoungJoo,
Lee KiBaek
Publication year - 2018
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2018-0066
Subject(s) - computer science , artificial intelligence , sign (mathematics) , convolutional neural network , sign language , process (computing) , deep learning , set (abstract data type) , artificial neural network , data set , machine learning , mathematics , mathematical analysis , linguistics , philosophy , programming language , operating system
This paper proposes a human‐like sign‐language learning method that uses a deep‐learning technique. Inspired by the fact that humans can learn sign language from just a set of pictures in a book, in the proposed method, the input data are pre‐processed into an image. In addition, the network is partially pre‐trained to imitate the preliminarily obtained knowledge of humans. The learning process is implemented with a well‐known network, that is, a convolutional neural network. Twelve sign actions are learned in 10 situations, and can be recognized with an accuracy of 99% in scenarios with low‐cost equipment and limited data. The results show that the system is highly practical, as well as accurate and robust.