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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References
Abed, H.E., Margner, V.: How to Improve Handwriting Recognition System. In: 2009 10th International Conference on Document Analysis and Recognition (ICDAR 2009). IEEE Computer Society (2009)
Amara, N.E.B., Bouslama, F.: Classification of Arabic Script Using Multiple Sources of Information: State of the Art and Perspectives. International Journal on Document Analysis and Recognition (IJDAR) 5, 195–212 (2003)
Azizi, N., Farah, N., Sellami, M., Ennaji, A.: Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition. In: El Gayar, N., Kittler, J., Roli, F. (eds.) MCS 2010. LNCS, vol. 5997, pp. 235–244. Springer, Heidelberg (2010)
Cheung, A., Bennamoun, M., Bergmann, N.W.: An Arabic Optical Character Recognition System Using Recognition-based Segmentation. The Journal of the Pattern Recognition 34, 215–223 (2001)
El-Hajj, R., Mokbel, C., Likforman-Sulem, L.: Combining Slanted-Frame Classifiers for Improved HMM-Based Arabic Handwriting Recognition. IEEE Transaction on Pattern Anaysis Machine Intelligence 31(7), 1165–1177 (2009)
Haboubi, S., Maddouri, S., Ellouze, N.: Primitive Invariant for Handwritten Arabic Script. International Journal of Computer and Information Science and Engineering 3 (2009)
Korsheed, M.S.: Off-Line Arabic Character Recognition – Survey. Pattern Analysis & Application 5, 31–45 (2002)
Lorigo, L.M., Govondaraju, V.: Offline Arabic Handwritting Recognition: A Survey. IEEE Transaction on Pattern Analysis Machine Intelligence 28(5), 712–724 (2006)
Mazani, M.: Jawi Handwritten Word Recognition System Using Recurrent Bama Neural Network. PhD Thesis, Universiti Kebangsaan Malaysia (2002)
Mohammad, F.N., Khairuddin, O., Mohamad, S.Z., Liong, C.Y.: Handwritten Cursive Jawi Character Recognition: A Survey. In: Fifth Int. Conf. on Comp. Graphic. Imaging and Visualization, pp. 247–256 (2008)
Mohammad, F.N.: Offline Jawi Handwritten recognition using trace transformation. Thesis Dr. of Philosophy. Universiti Kebangsaan Malaysia (2010)
Nor’aiza, A.: Online Non-cursive Jawi Character Recognition system. Thesis Ms., Universiti Malaya (2007)
Oh, I.S., Lee, J.S., Suen, C.Y.: Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10), 1089–1094 (1999)
Ramteke, R.J., Mehrotra, S.C.: Feature Extraction Base on Moment Invariants For Handwriting Recognition. In: Conference on Cybernatics and Intelligent Systems, pp. 1–6 (2006)
Razak, Z., Ghani, N.A., Tamil, E.M., Idris, M.Y., Noor, N.M., Salleh, R., Yaacob, M., Yakub, M., Yusoff, Z.B.M.: Off-Line Jawi Handwriting Recognition Using Hamming Classsification. Information Technology Journal 8(7), 971–981 (2009)
Remon, R., Khairuddin, O., Mohammad, F.N.: Handwritten Jawi Words Recognition Using Hidden Markov Models. In: International Simposium on InformationTechnology, ITSim 2008, vol. 2, pp. 1–5 (2008)
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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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