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Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks

Jose Guadalupe Beltran-Hernandez Jose Ruiz-Pinales Pedro Lopez-Rodriguez Jose Luis Lopez-Ramirez Juan Gabriel Avina-Cervantes

*Corresponding author: Jose Ruiz-Pinales pinales@ugto.mx


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.

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