Transliteration of Hiragana and Katakana Handwritten Characters Using CNN-SVM

Hiragana and katakana handwritten characters are often used when writing words in Japanese. Japanese itself is often used by native Japanese as well as people learning Japanese around the world. Hiragana and katakana characters themselves are difficult to learn because many characters are similar to one another. In this study, hiragana and basic katakana, dakuten, handakuten, and youon were used, which were taken from the respondents using a questionnaire. This study used the CNN method which will be compared with a combination of the CNN and SVM methods which have been designed to identify each character that has been prepared. Preprocessing of character images uses the methods of image resizing, grayscaling, binarization, dilation, and erosion. The preprocessed results will be input for CNN as a feature extraction tool and SVM as a tool for character recognition. The results of this study obtained accuracy with the following parameters: 69×69 image size, 3 patience values, val_loss monitor callbacks, Nadam optimization function, 0.001 learning rate value, 30 epochs value, and SVM RBF kernel. If using a system that only uses the CNN network, the accuracy is 87.82%. The results obtained when using a combination of CNN and SVM were 88.21%

TRANSLITERASI CITRA AKSARA HIRAGANA MEMPERGUNAKAN JARINGAN BACKPROPAGATION

The purpose of this research is to understand the ability and to find out how much percentage of accuracy of Back Propagation algorithm in Japanese characters handwriting Hiragana’s pattern recognition. This study used the characterization of calculating a black pixel (intensity of character) and calculating the direction by applying masking diagonal left, right diagonal, vertical, and horizontal (mark direction). There were 797 letters that can be read correctly by backpropagation test equipment after applying feature combination 6 on the 7th test. Combination 6 had 5 charaterization feature 1, feature 2, feature 3, feature 4, and feature 5. Each feature was a colective feature of 9 segment which had explanation like this : feature 1 was Intensity of Character (Black), feature 2 was Mark Direction Diagonal 1 (Diag1), feature 3 was Mark Direction Diagonal 2 (Diag2), feature 4 was Mark Direction Horisontal (Horz) , and feature 5 was Mark Direction Vertical (Vert). This study revealed succeeded in proving that thebackpropagation algorithm was able to recognize Hiragana letters after achieving a success rate of accuracy above 85% which was 86.63%.