Open AccessMulti-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks
Author(s) -
José Guadalupe Beltrán-Hernández,
José Ruiz-Pinales,
Pedro López-Rodríguez,
José Luis López-Ramírez,
Juan Gabriel Aviña-Cervantes
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020293
Subject(s) - handwriting , computer science , intelligent character recognition , convolutional neural network , handwriting recognition , speech recognition , artificial intelligence , feature extraction , artificial neural network , character (mathematics) , feature (linguistics) , pattern recognition (psychology) , character recognition , philosophy , geometry , mathematics , image (mathematics) , linguistics
Despite the increasing use of technology, handwriting has remained to date as an efficient means of communication. Certainly, handwriting is a critical motor skill for childrens cognitive development and academic success. This article presents a new methodology based on electromyographic signals to recognize multi-user free-style multi-stroke handwriting characters. The approach proposes using powerful Deep Learning (DL) architectures for feature extraction and sequence recognition, such as convolutional and recurrent neural networks. This framework was thoroughly evaluated, obtaining an accuracy of 94.85%. The development of handwriting devices can be potentially applied in the creation of artificial intelligence applications to enhance communication and assist people with disabilities.