CNN-based Baybayin Character Recognition on Android System

Edric Castel C. Hao, Galvin Brice Lim, Melvin K. Cabatuan, EDWIN SYBINGCO, Ann Dulay

Abstract: Baybayin is an old Philippine script that has become part of the Filipino heritage; however, due to the difficulty of the writing system, most people nowadays do not use it anymore. That is why this paper proposed an improved CNN model for classifying the Philippine Script Baybayin in order to help people to learn this old script. The model was systematically developed through experimentation and hyperparameter tuning with MobileNetV2 as the baseline. Sample handwritten Baybayin characters were obtained from Kaggle and Mendeley Data for training. It was evaluated that the proposed model has training and validation accuracies of 97.04% and 96.02%, respectively, in contrast to the MobileNetV2, which has slightly lower accuracies and larger fluctuations in its validation accuracy. Aside from that, this model only has total parameters of 892,255 and a size of 3575.944 kB. Lastly, through mobile deployment testing, it was concluded that only the proposed model is working, as the MobileNetV2 was not able to accurately detect the diacritics.