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IJMLC 2020 Vol.10(2): 265-270 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.930

Baybáyin Character Recognition Using Convolutional Neural Network

James Arnold Nogra, Cherry Lyn Sta Romana, and Elmer Maravillas

Abstract—With the Philippine Congress approving the House Bill 1022 which states that the Baybáyin will be used as the national writing system of the country, the Department of Education and National Commission for Culture and Arts has vowed to reintroduce this old writing system back to the country. In order to hasten up the learning process, a convolutional neural network is designed to check the classification of hand-drawn characters. Nine convolutional neural network models were designed to check which is the best for this type of character recognition. From the 7000 hand-drawn Baybáyin characters used for training, it has found out that the best neural network for this type of classification is composed of three convolutional layers with 32 channels, 64 channels, and 128 channels respectively using 3x3 filters. The final model has also three max pooling layers right after each convolution layer with 2x2 size and two fully connected layers at the end. The number of output of this neural network model is 63 which is the same as the total number of Baybáyin characters. The model yields a 94% accuracy rate using the validation data. The other 8 CNN models also did well with accuracy rates ranging from 57% to 92%.

Index Terms—Baybáyin, character recognition, convolutional neural network, deep neural network, Tensorflow.

J. Nogra is with the Information Technology Department at Cebu Institute of Technology – University, Philippines (e-mail: jamesnogra@gmail.com).
C. Sta Romana and E. Maravillas are with the College of Computer Studies at Cebu Institute of Technology - University (e-mail: cstaromana@gmail.com, elmer.maravillas@gmail.com).


Cite: James Arnold Nogra, Cherry Lyn Sta Romana, and Elmer Maravillas, "Baybáyin Character Recognition Using Convolutional Neural Network," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 265-270, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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