CNN for Image Identification of Hiragana Based on Pattern Recognition using CNN

Chaerul Umam (Universitas Dian Nuswantoro)
Andi Danang Krismawan (Universitas Dian Nuswantoro)
Rabei Raad Ali (Tun Hussein Onn Malaysia University)

JAIS (Journal of Applied Intelligent System)
Vol 6, No 2 (2021): Journal of Applied Intelligent System

Hiragana is one of the letters in Japanese. In this study, CNN (Convolutional Neural Network) method used as identication method, while he preprocessing used thresholding. Then carry out the normalization stage and the filtering stage to remove noise in the image. At the training stage use maxpooling and danse methods as a liaison in the training process, wherea in testing stage using the Adam Optimizer method. Here, we use 1000 images from 50 hiragana characters with a ratio of 950: 50, 950 as training data and 50 data as testing data. Our experiment yield accuracy in 95%.