Open AccessCNN for Image Identification of Hiragana Based on Pattern Recognition using CNN
Author(s) -
Chaerul Umam,
Andi Danang Krismawan,
Rabei Raad Ali
Publication year - 2021
Publication title -
jais (journal of applied intelligent system)
Language(s) - English
Resource type - Journals
eISSN - 2503-0493
pISSN - 2502-9401
DOI - 10.33633/jais.v6i2.4586
Subject(s) - preprocessor , thresholding , computer science , artificial intelligence , normalization (sociology) , convolutional neural network , pattern recognition (psychology) , image (mathematics) , speech recognition , computer vision , sociology , anthropology
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%.