Japanese character (Kana) pattern recognition application using neural network

Japanese language has complex writing systems, Kanji and Kana (Katakana and Hiragana). Each one has different style of writing. One simple way to differentiate is Kanji have more strokes than Kana. Meanwhile, it needs a lot of effort to remember characters of Katakana and Hiragana, thus it will be very difficult to distinguish handwritten Katakana and Hiragana, since there are a lot of similar characters. This is the reason why we need pattern recognition. Handwriting recognition has been the object of intensive research during the last decades, including Japanese characters recognition. In this research, we build an application to recognize Kana´s character handwriting. Pattern recognition on Kana is started based on Optical Character Recognition (OCR) consist of scan image, preprocessing, feature extraction and post-processing. Classification process will be done in post-processing using neural network back propagation. The processing of input images, involves training and testing image. Training image uses 3.956 characters done in three phases of training. Testing image uses 552 characters of some images inputted in training phase and image which has not been used before. The test result achieve maximum accuracy of recognition system at 91.88% from train image and 31.03% from test image.