Abstract
Feature extraction is an important stage in Jawi recognition system because it can influence various aspects that can affect the recognition performance. Statistical feature extraction is strongly influenced by the presence of pixels that make up a word, especially for technique based on zonings and pixel density. Variability in writing style makes the presence of pixels that form the smallest primitive structure in a zone becomes less uniform and this affect the value of pixel density. To overcome this problem, a technique known as numeric code representation to represent the range of the primitive structure tilt in a zone has been proposed. Numeric code is generated by comparing average row and column of smallest primitive structure in each zone. The experimental results show that the numeric code representation is the best method in representing the main structure of the Jawi sub-word image when compared with the other three feature representation techniques. This is because it can generate the highest recognition rate for both classifiers which is used either based on probability or voting.
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Mohamad, R., Manaf, M., Rauf, R.H.A., Nasruddin, M.F. (2015). Main Structure of Handwritten Jawi Sub-word Representation Using Numeric Code. In: Berry, M., Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2015. Communications in Computer and Information Science, vol 545. Springer, Singapore. https://doi.org/10.1007/978-981-287-936-3_20
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DOI: https://doi.org/10.1007/978-981-287-936-3_20
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