DOI : https://doi.org/10.18517/ijaseit.11.1.10113

Trace Transform Feature Learning for Offline Jawi Handwritten Recognition

Anton Heryanto Hasan (1), Khairuddin Omar (2), Muhammad Faidzul Nasrudin (3)
(1) Faculty Technology and Information Science, Universiti Kebangsaan Malaysia, Bandar Baru Bangi, 43650, Malaysia
(2) Faculty Technology and Information Science, Universiti Kebangsaan Malaysia, Bandar Baru Bangi, 43650, Malaysia
(3) Faculty Technology and Information Science, Universiti Kebangsaan Malaysia, Bandar Baru Bangi, 43650, Malaysia
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How to cite (IJASEIT) :
Hasan, Anton Heryanto, et al. “Trace Transform Feature Learning for Offline Jawi Handwritten Recognition”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 37-42, doi:10.18517/ijaseit.11.1.10113.
Citation Format :
Offline Jawi handwritten recognition is very important to allow efficient archiving and retrieving the original documents and increase the availability of the content. It is challenging task and still considered an open problem because the state-of-the-art recognizer performance is considered sub-par. The tradition trace Transform features extractor has potential, however the complexity of parameters tuning in feature engineered approach combine with independent non-learnable sub-word classifier produce sub-par Jawi sub-word recognition accuracy. The proposed trace Transform feature learning address the features extraction complexity by automatically discovers the features according to data. The features extractor and classifier trained end-to-end from raw input data to target class to find the optimum parameters. The trace transform process defined as layer similar with convolution process in Convolution Neural Network. This approach improves data representation and produce better Jawi handwritten recognition performance. trace Transform feature learning are more robust to Affine Transformations compared to the state-of-the-arts Convolution Neural Networks feature learning because its data representation invariant to rotation, slanting and skewing. This proposed feature learning performance evaluated with its performance in sub-word recognition performance using Jawi dataset. In this paper only single layer of trace transform feature learning compare with traditional trace transform feature and Convolution Neural network as the state-of-the-art feature learning. The performances are significantly better compared to traditional trace transform feature and able to compete with convolution neural network in single layer, three layers and comparable with eight layers.

Muhammad Faidzul Nasrudin, Khairuddin Omar, Muhammad Shanudin Zakaria, and Liong, C.Y, “Handwritten cursive jawi character recognition: A survey,” in Proceedings - computer graphics, imaging and visualisation, modern techniques and applications, cgiv, 2008, pp. 247-256.

Khairuddin Omar, “Jawi Handwritten Text Recognition Using Multi-Level Classifier (in Malay),” PhD thesis, Universiti Putra Malaysia, 2000.

Mazani Manaf, “Jawi Handwritten Text Recognition Using Recurrent Bama Neural Networks (in Malay),” PhD thesis, Universiti Kebangsaan Malaysia, 2002.

Anton Heryanto, Mohammad Faidzul Nasrudin, and Khairuddin Omar, “Offline jawi handwritten recognizer using hybrid artificial neural networks and dynamic programming,” in Proceedings of the international symposium on information technology 2008 2, 2008.

Remon Redika, Khairuddin Omar, and Mohammad Faidzul Nasrudin, “Handwritten jawi words recognition using hidden markov models,” in Proceedings of the international symposium on information technology 2008 2, 2008.

Mohd Sanusi Bin Azmi, “A Novel Feature from Combinations of Triangle Geometry For Digital Jawi Paleography,” PhD thesis, Universiti Kebangsaan Malaysia, 2013.

Mohammad Faidzul Nasrudin, “Offline Jawi Handwritten Recognition Using Trace Transform (in Malay),” PhD thesis, Universiti Kebangsaan Malaysia, 2010.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84-90, May 2017.

G. W. Cottrell and P. Munro, “Principal components analysis of images via back propagation,” in Visual communications and image processing’88: Third in a series, 1988, vol. 1001, pp. 1070-1078.

N. Jaitly and G. E. Hinton, “Learning a better representation of speech soundwaves using restricted boltzmann machines,” 2011.

N. Y. Hammerla, T. Plí¶tz, S. Vajda, and G. A. Fink, “Towards feature learning for hmm-based offline handwriting recognition,” in International workshop on frontiers in arabic handwriting recognition, 2010.

M. Ranzato, C. Poultney, S. Chopra, and Y. LeCun, “Efficient learning of sparse representations with an energy-based model,” in Proceedings of the 19th international conference on neural information processing systems, 2006, pp. 1137-1144.

S. Rifai, G. Mesnil, P. Vincent, X. Muller, Y. Bengio, Y. Dauphin, and X. Glorot, “Higher order contractive auto-encoder,” in Joint european conference on machine learning and knowledge discovery in databases, pp. 645-660.

S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio, “Contractive auto-encoders: Explicit invariance during feature extraction,” in Proceedings of the 28th international conference on international conference on machine learning, pp. 833-840.

P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in Proceedings of the 25th international conference on machine learning, 2008, pp. 1096-1103.

B. A. Olshausen and D. J. Field, “Emergence of simple-cell receptive field properties by learning a sparse code for natural images,” Nature, vol. 381, no. 6583, p. 607, 1996.

Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation, vol. 1, no. 4, pp. 541-551, 1989.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR, 2016.

S. Luan, B. Zhang, C. Chen, X. Cao, J. Han, and J. Liu, “Gabor convolutional networks,” CoRR, vol. abs/1705.01450, 2017.

A. Kadyrov and M. Petrou, “The trace transform and its applications,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 8, pp. 811-828, Aug. 2001.

S. R. Deans, the radon transforms and some of its applications. Krieger Publishing Company, 1983.

P. Toft, “The Radon Transform - Theory and Implementation,” PhD thesis, Department of Mathematical Modelling, Technical University of Denmark, 1996.

A. Kadyrov and M. Petrou, “Object descriptors invariant to affine distortions,” in Proceedings of the the british. Machine vision conference bmvc2001, 2001.

A. Kadyrov and M. Petrou, “Affine parameter estimation from the trace transform,” IEEE transactions on pattern analysis and machine intelligence, vol. 28, pp. 1631-45, Nov. 2006.

B. S. Shin, E. Y. Cha, K. W. Cho, R. Klette, and Y. W. Woo, “Effective feature extraction by trace transform for insect footprint recognition,” 2008.

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