Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

Satiabudhi, Gregorius and Adipranata, Rudy (2014) Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods. Journal of ICT Research and Applications, 8 (3). pp. 195-212. ISSN 2337-5787

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        Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.

        Item Type: Article
        Uncontrolled Keywords: backpropagation; bidirectional associative memory; chi2; counterpropagation; evolutionary neural network; Javanese character recognition
        Subjects: Q Science > QA Mathematics > QA76 Computer software
        Divisions: Faculty of Industrial Technology > Informatics Engineering Department
        Depositing User: Admin
        Date Deposited: 08 Jul 2015 01:38
        Last Modified: 18 Dec 2019 09:00
        URI: http://repository.petra.ac.id/id/eprint/17082

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