PENGENALAN TULISAN TANGAN HANGUL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK - Oktavianus, Oktavianus; Lubis, Chairisni; Hendryli, Janson (Jurnal Ilmu Komputer dan Sistem Informasi, 2021)
The effect of the Korean Culture for the past few years has been increasing whereas the culture has been a part of the people’s daily lives especially for the youth. Unlike the Latin alphabet, the Hangul alphabet has the characteristics resembling strokes that’re written in blocks that make a syllable. Therefore, on this occasion a system will be made to recognize Hangul as an alternative for learning Hangul. The application design uses pre-processing such as grayscaling, thresholding, dilation and contouring. The data collected in this design uses as much as 3.960 images of the Hangul Alphabet. After GAN is used to generate images as well as Data Augmentation, the dataset reaches a total of 5.303 images which are separated into training set and testing set. The testing is done 2 times whereas the first test is tested on single letters and reached 55,58% accuracy. The second test is done with the letters that got segmented by the application which consists of 1-4 syllables whereas it reached 55,7-60% accuracy.