Artículo
Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks
Autor/es | Beltrán Hernández, José Guadalupe
Ruiz Pinales, José López Rodríguez, Pedro López Ramírez, José Luis Aviña Cervantes, Juan Gabriel |
Departamento | Universidad de Sevilla. Departamento de Análisis Matemático |
Fecha de publicación | 2020-08-12 |
Fecha de depósito | 2023-04-25 |
Publicado en |
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Resumen | 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. ... 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. |
Cita | Beltrán Hernández, J.G., Ruiz Pinales, J., López Rodríguez, P., López Ramírez, J.L. y Aviña Cervantes, J.G. (2020). Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 17 (5), 5432-5448. https://doi.org/10.3934/mbe.2020293. |
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Multi-Stroke.pdf | 4.521Mb | [PDF] | Ver/ | |