Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods
| Main Authors: | Budhi, Gregorius Satia; Informatics Department, Petra Christian University Jalan Siwalankerto 121-131, Surabaya 60236, Adipranata, Rudy; Informatics Department, Petra Christian University Jalan Siwalankerto 121-131, Surabaya 60236 |
|---|---|
| Other Authors: | DIPA-PT Coordination of Private Higher Education, Research Center Petra Christian University |
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
ITB Journal Publisher, LPPM ITB
, 2015
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| Subjects: | |
| Online Access: |
http://journals.itb.ac.id/index.php/jictra/article/view/769 http://journals.itb.ac.id/index.php/jictra/article/view/769/852 http://journals.itb.ac.id/index.php/jictra/article/downloadSuppFile/769/130 http://journals.itb.ac.id/index.php/jictra/article/downloadSuppFile/769/131 |
Daftar Isi:
- 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.