Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

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.