Farsi Handwritten Digits Recognition based on Interval Type-II Fuzzy Fusion of Support Vector Machines

Document Type : Original Article

Authors

Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

Abstract

The problem of automatic handwritten context recognition has received considerable attention of many researchers. In this paper, a fusion system is proposed to enhance the recognition accuracy of Farsi handwritten digits. The proposed approach consists of a prepration process and two main phases. In the prepration process, some pre-processing operations are performed on the image. Then some features are extracted, among which a multi-objective particle swarm optimization selects more effective ones. For every image, these optimal features are given as the input data to the classifiers. In the first main phase, training datasets are used to construct three different SVMs. In order to achieve better results, the adaptive best-mass gravitational search algorithm is utilized to adjust the SVMs parameters. In the second main phase, an interval type–II fuzzy inference system receives the SVMs outputs and by combining them, it presents a more accurate estimation of the digit in the image. The results of applying the proposed approach to the problem of scanned Farsi handwritten digits in the standard HODA database demonstrated that this algorithm attains high accuracy, precision and recall performance indices, comparing to other existing methods.

Keywords


[1]   C.-L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, "Handwritten digit recognition: benchmarking of state-of-the-art techniques," Pattern recognition, vol. 36, no. 10, pp. 2271-2285, 2003.
[2]   R. Al-Jawfi, "Handwriting Arabic character recognition LeNet using neural network," Int. Arab J. Inf. Technol., vol. 6, no. 3, pp. 304-309, 2009.
[3]   C. A. Rahman, W. Badawy, and A. Radmanesh, "A real time vehicle's license plate recognition system," in Advanced Video and Signal Based Surveillance, 2003. Proceedings. IEEE Conference on, 2003, pp. 163-166: IEEE.
[4]   S. N. Srihari and E. J. Kuebert, "Integration of hand-written address interpretation technology into the united states postal service remote computer reader system," in Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on, 1997, vol. 2, pp. 892-896: IEEE.
[5]   Y. Le Cun, L. Bottou, and Y. Bengio, "Reading checks with multilayer graph transformer networks," in Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on, 1997, vol. 1, pp. 151-154: IEEE.
[6]   M. Dehghan and K. Faez, "Farsi handwritten character recognition with moment invariants," in Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on, 1997, vol. 2, pp. 507-510: IEEE.
[7]   D. Keysers, T. Deselaers, C. Gollan, and H. Ney, "Deformation models for image recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1422-1435, 2007.
[8]   F. Lauer, C. Y. Suen, and G. Bloch, "A trainable feature extractor for handwritten digit recognition," Pattern Recognition, vol. 40, no. 6, pp. 1816-1824, 2007.
[9]   W. Pan, T. D. Bui, and C. Y. Suen, "Isolated handwritten Farsi numerals recognition using sparse and over-complete representations," in Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on, 2009, pp. 586-590: IEEE.
[10] P. Zhang, T. D. Bui, and C. Y. Suen, "A novel cascade ensemble classifier system with a high recognition performance on handwritten digits," Pattern Recognition, vol. 40, no. 12, pp. 3415-3429, 2007.
[11] H. Soltanzadeh and M. Rahmati, "Recognition of Persian handwritten digits using image profiles of multiple orientations," Pattern Recognition Letters, vol. 25, no. 14, pp. 1569-1576, 2004.
[12] A. Alaei, U. Pal, and P. Nagabhushan, "Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals," in Advances in Pattern Recognition, 2009. ICAPR'09. Seventh International Conference on, 2009, pp. 391-394: IEEE.
[13] J. Sadri, C. Y. Suen, and T. D. Bui, "Application of support vector machines for recognition of handwritten Arabic/Persian digits," in Proceedings of Second Iranian Conference on Machine Vision and Image Processing, 2003, vol. 1, pp. 300-307.
[14] H. Salimi and D. Giveki, "Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule," International Journal on Document Analysis and Recognition (IJDAR), vol. 16, no. 4, pp. 371-386, 2013.
[15] N. A. Arbain, M. S. Azmi, A. K. Muda, N. A. Muda, A. R. J. I. J. o. C. I. S. Radzid, and I. M. Applications, "Offline handwritten digit recognition using triangle geometry properties," vol. 10, pp. 87-97, 2018.
[16] M. S. Azmi, K. Omar, M. F. Nasrudin, and A. K. Muda, "Fitur Baharu Dari Kombinasi Geometri Segitiga Dan Pengezonan Untuk Paleografi Jawi Digital," Universiti Kebangsaan Malaysia, 2013.
[17] M. J. Parseh, M. J. I. J. o. I. Meftahi, and Graphics, "A new combined feature extraction method for Persian handwritten digit recognition," vol. 17, no. 02, p. 1750012, 2017.
[18] M. Mohammadpoor, A. Mehdizadeh, and H. A. J. M. J. o. E. E. Noghabi, "A novel method for persian handwritten digit recognition using support vector machine," vol. 12, no. 3, pp. 63-67, 2018.
[19] M. Ziaratban, K. Faez, and F. Faradji, "Language-based feature extraction using template-matching in Farsi/Arabic handwritten numeral recognition," in Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, 2007, vol. 1, pp. 297-301: IEEE.
[20] S. Khorashadizadeh and A. Latif, "Arabic/Farsi Handwritten Digit Recognition using Histogram of Oriented Gradient and Chain Code Histogram," International Arab Journal of Information Technology (IAJIT), vol. 13, no. 4, 2016.
[21] R. Safdari and M.-S. Moin, "A hierarchical feature learning for isolated Farsi handwritten digit recognition using sparse autoencoder," in Artificial Intelligence and Robotics (IRANOPEN), 2016, 2016, pp. 67-71: IEEE.
[22] R. Hajizadeh, A. Aghagolzadeh, and M. Ezoji, "Fusion of LLE and stochastic LEM for Persian handwritten digits recognition," International Journal on Document Analysis and Recognition (IJDAR), vol. 21, no. 1-2, pp. 109-122, 2018.
[23] Z. Sadeghi and A. Testolin, "Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning," Cognitive processing, vol. 18, no. 3, pp. 273-284, 2017.
[24] Y. Zamani, Y. Souri, H. Rashidi, and S. Kasaei, "Persian handwritten digit recognition by random forest and convolutional neural networks," in Machine Vision and Image Processing (MVIP), 2015 9th Iranian Conference on, 2015, pp. 37-40: IEEE.
[25] A. Broumandnia, J. Shanbehzadeh, and M. R. Varnoosfaderani, "Persian/arabic handwritten word recognition using M-band packet wavelet transform," Image and Vision Computing, vol. 26, no. 6, pp. 829-842, 2008.
[26] S. Rajashekararadhya, P. V. Ranjan, and V. M. Aradhya, "Isolated handwritten Kannada and Tamil numeral recognition: A novel approach," in 2008 First International Conference on Emerging Trends in Engineering and Technology, 2008.
[27] S. Wshah, Z. Shi, and V. Govindaraju, "Segmentation of Arabic handwriting based on both contour and skeleton segmentation," in Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on, 2009, pp. 793-797: IEEE.
[28] J. Yang, D. Zhang, A. F. Frangi, and J.-y. Yang, "Two-dimensional PCA: a new approach to appearance-based face representation and recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 1, pp. 131-137, 2004.
[29] K. Khurshid, I. Siddiqi, C. Faure, and N. Vincent, "Comparison of Niblack inspired binarization methods for ancient documents," in Document Recognition and Retrieval XVI, 2009, vol. 7247, p. 72470U: International Society for Optics and Photonics.
[30] D. Deodhare, N. R. Suri, and R. Amit, "Preprocessing and Image Enhancement Algorithms for a Form-based Intelligent Character Recognition System," IJCSA, vol. 2, no. 2, pp. 131-144, 2005.
[31] N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 886-893: IEEE.
[32] M. Clerc and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002.
[33] V. Pareto, Cours d'économie politique. Librairie Droz, 1964.
[34] C. Coello Coello and M. Lechuga, "MOPSO: a proposal for multiple objective particle swarm optimization," in Proc., Evolutionary Computation, 2002. CEC'02. Proceedings of the 2002 Congress on, pp. 1051-1056.
[35] O. Rashnodi et al., "Persian Handwritten Digit Recognition Using Support Vector Machines," International Journal of Computer Applications, vol. 29, no. 12, pp. 1-6, 2011.
[36] S. Abe, "Fuzzy support vector machines for multilabel classification," Pattern Recognition, vol. 48, no. 6, pp. 2110-2117, 2015.
[37] X. Zhang, D. Qiu, and F. Chen, "Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis," Neurocomputing, vol. 149, pp. 641-651, 2015.
[38] L. Saidi, J. B. Ali, and F. Fnaiech, "Application of higher order spectral features and support vector machines for bearing faults classification," ISA transactions, vol. 54, pp. 193-206, 2015.
[39] M. R. Mosavi, M. Khishe, M. J. Naseri, G. R. Parvizi, and A. Mehdi, "Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset," Archives of Acoustics, vol. 44, no. 1, pp. 137–151, 2019.
[40] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, no. 13, pp. 2232-2248, 2009.
[41] R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, vol. 14, no. 2, pp. 1137-1145: Montreal, Canada.
[42] X. Chen, Y. Li, R. Harrison, and Y.-Q. Zhang, "Type-2 fuzzy logic-based classifier fusion for support vector machines," Applied Soft Computing, vol. 8, no. 3, pp. 1222-1231, 2008.
[43] H. Chaoui and P. Sicard, "Adaptive fuzzy logic control of permanent magnet synchronous machines with nonlinear friction," IEEE Transactions on Industrial Electronics, vol. 59, no. 2, pp. 1123-1133, 2012.
[44] T. Nguyen, A. Khosravi, D. Creighton, and S. Nahavandi, "EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems," Expert Systems with Applications, vol. 42, no. 9, pp. 4370-4380, 2015.
[45] J. M. Mendel and R. B. John, "Type-2 fuzzy sets made simple," IEEE Transactions on fuzzy systems, vol. 10, no. 2, pp. 117-127, 2002.
[46] C. Wagner and H. Hagras, "Toward general type-2 fuzzy logic systems based on zSlices," IEEE Transactions on Fuzzy Systems, vol. 18, no. 4, pp. 637-660, 2010.
[47] H. Khosravi and E. Kabir, "Introducing a very large dataset of handwritten Farsi digits and a study on their varieties," Pattern recognition letters, vol. 28, no. 10, pp. 1133-1141, 2007.
[48] A. Ekbal and S. Saha, "Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition," Soft Computing, vol. 17, no. 1, pp. 1-16, 2013.