Kyouiku Kanji Grade 1 Recognition Using MobileNet V2 Based on Android

Character recognition has become a popular research topic in the field of pattern recognition and machine learning, including handwriting recognition, specifically kanji handwriting. This study performs handwriting recognition of kyouiku kanji grade 1, which is the kanji required to be learnt by grade 1 elementary school students in Japan. This research uses ETL-9B dataset from Electrotechnical Laboratory (now AIST), uses CNN MobileNet V2 deep learning method that has been customized for mobile devices, and uses Android application as the user interface implementation. Based on the study results, the highest accuracy model was obtained with an accuracy of 96,6875% and a size of 27.4MB for the alpha 1.0 hyperparameter. It can be concluded that the CNN MobileNet V2 deep learning method has performed quite well in the process of recognizing handwritten kyouiku kanji grade 1.