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Javier Orlando Pinzón-Arenas
Nueva Granada Military University
Computer Science & IT Electrical & Electronics Engineering

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Comparison between handwritten word and speech record in real-time using CNN architectures Javier Orlando Pinzón-Arenas; Robinson Jiménez-Moreno
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.44 KB) | DOI: 10.11591/ijece.v10i4.pp4313-4321

Abstract

This paper presents the development of a system of comparison between words spoken and written by means of deep learning techniques. There are used 10 words acquired by means of an audio function and, these same words, are written by hand and acquired by a webcam, in such a way as to verify if the two data match and show whether or not it is the required word. For this, 2 different CNN architectures were used for each function, where for voice recognition, a suitable CNN was used to identify complete words by means of their features obtained with mel frequency cepstral coefficients, while for handwriting, a faster R-CNN was used, so that it both locates and identifies the captured word. To implement the system, an easy-to-use graphical interface was developed, which unites the two neural networks for its operation. With this, tests were performed in real-time, obtaining a general accuracy of 95.24%, allowing showing the good performance of the implemented system, adding the response speed factor, being less than 200 ms in making the comparison.
Assistant robot through deep learning Robinson Jiménez-Moreno; Javier Orlando Pinzón-Arenas; César Giovany Pachón-Suescún
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (918.575 KB) | DOI: 10.11591/ijece.v10i1.pp1053-1062

Abstract

This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.
Co-Authors César Giovany Pachón-Suescún Robinson Jiménez-Moreno