Abstract

Hiragana is one of the traditional Japanese letters used to translate native Japanese words. The introduction of an object requires a learning process, which is obtained through the characteristic in the form of unique features on similar objects, but manually it is quite difficult to distinguish these letters. This writing explains the discussion system to differentiate between hiragana letters starting from preprocess namely grayscale and threshold, then segmenting and normalization, while image classification uses the Template Matching Correlation method. The results of tests carried out assessing the test rate of around 76% using the Matching Template Correlation method. While the remaining 14% indicates that the object identified does not match the intended results.