Handwritten character recognition using wavelet energy and extreme learning machine

This paper deals with the recognition of handwritten Malayalam character using wavelet energy feature (WEF) and extreme learning machine (ELM). The wavelet energy (WE) is a new and robust parameter, and is derived using wavelet transform. It can reduce the influences of different types of noise at different levels. WEF can reflect the WE distribution of characters in several directions at different scales. To a non oscillating pattern, the amplitudes of wavelet coefficients increase when the scale of wavelet decomposition increase. WE of different decomposition levels have different powers to discriminate the character images. These features constitute patterns of handwritten characters for classification. The traditional learning algorithms of the different classifiers are far slower than required. So we have used an extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms for feed forward neural networks. This feature vector, classifier combination gave good recognition accuracy at level 6 of the wavelet decomposition.

[1]  Nikolai Gorski,et al.  The A2iA Intercheque System: Courtesy Amount and Legal Amount Recognition for French Checks , 1997, Int. J. Pattern Recognit. Artif. Intell..

[2]  Majid Ahmadi,et al.  Recognition of handwritten numerals with multiple feature and multistage classifier , 1995, Pattern Recognit..

[3]  Ka-Chung Leung,et al.  Recognition of Handwritten Chinese Characters by Combining Regularization, Fisher's Discriminant and Distorted Sample Generation , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[4]  A. Dehghani,et al.  Off-line recognition of isolated Persian handwritten characters using multiple hidden Markov models , 2001, Proceedings International Conference on Information Technology: Coding and Computing.

[5]  Ka-Chung Leung,et al.  Recognition of handwritten Chinese characters by critical region analysis , 2010, Pattern Recognit..

[6]  Apurva A. Desai,et al.  Gujarati handwritten numeral optical character reorganization through neural network , 2010, Pattern Recognit..

[7]  Hiroshi Sako,et al.  Performance evaluation of pattern classifiers for handwritten character recognition , 2002, International Journal on Document Analysis and Recognition.

[8]  M. B. Sukhaswami,et al.  Recognition of telugu characters using neural networks , 1995, Int. J. Neural Syst..

[9]  Vibhav Kumar Sachan,et al.  Convergence towards Packet Networks , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[10]  Amaury Lendasse,et al.  OP-ELM: Optimally Pruned Extreme Learning Machine , 2010, IEEE Transactions on Neural Networks.

[11]  Binu P. Chacko,et al.  Discrete Curve Evolution Based Skeleton Pruning for Character Recognition , 2009, 2009 Seventh International Conference on Advances in Pattern Recognition.

[12]  Alexander Shustorovich,et al.  A subspace projection approach to feature extraction: The two-dimensional gabor transform for character recognition , 1994, Neural Networks.

[13]  Lajish.  Adaptive neuro fuzzy inference based pattern recognition studies on handwritten character images , 2007 .

[14]  Yuan Yan Tang,et al.  Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network , 1996, Pattern Recognit..

[15]  Alessandro Sperduti,et al.  Theoretical and Experimental Analysis of a Two-Stage System for Classification , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Edwin R. Hancock,et al.  Learning mixtures of point distribution models with the EM algorithm , 2003, Pattern Recognit..

[17]  Richard M. Schwartz,et al.  A Script-Independent Methodology For Optical Character Recognition , 1998, Pattern Recognit..

[18]  Ting-Hua Yi,et al.  Multi-component seismic response analysis of offshore platform by wavelet energy principle , 2009 .

[19]  Cheng-Lin Liu,et al.  Pseudo two-dimensional shape normalization methods for handwritten Chinese character recognition , 2005, Pattern Recognit..

[20]  Jianchang Mao,et al.  Improving OCR performance using character degradation models and boosting algorithm , 1997, Pattern Recognit. Lett..

[21]  Bindu S Moni,et al.  Modified quadratic classifier for Handwritten Malayalam Character recognition using Run Length Count , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[22]  Lei Huang,et al.  Handwritten Chinese character recognition method based on non-parametric dimensionality reduction , 2010, 2010 International Conference On Computer Design and Applications.

[23]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[24]  Binu P. Chacko,et al.  Pre and Post Processing Approaches in Edge Detection for Character Recognition , 2010, 2010 12th International Conference on Frontiers in Handwriting Recognition.

[25]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[26]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[27]  Hiroshi Sako,et al.  Handwritten Chinese character recognition: alternatives to nonlinear normalization , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[28]  Madasu Hanmandlu,et al.  Fuzzy model based recognition of handwritten numerals , 2007, Pattern Recognit..

[29]  Sundaram Suresh,et al.  Performance enhancement of extreme learning machine for multi-category sparse data classification problems , 2010, Eng. Appl. Artif. Intell..

[30]  Suban G. Krishnamoorthy,et al.  Recognition of handprinted Tamil characters , 1980, Pattern Recognit..

[31]  G. Raju,et al.  Wavelet Transform and Projection Profiles in Handwritten Character Recognition - A Performance Analysis , 2008, 2008 16th International Conference on Advanced Computing and Communications.

[32]  Guang-Bin Huang,et al.  Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.

[33]  Meng Shi,et al.  Handwritten numeral recognition using gradient and curvature of gray scale image , 2002, Pattern Recognit..

[34]  Fumitaka Kimura,et al.  Handwritten ZIP code recognition using lexicon free word recognition algorithm , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[35]  Zexuan Zhu,et al.  A fast pruned-extreme learning machine for classification problem , 2008, Neurocomputing.

[36]  D. Guillevic,et al.  HMM-KNN word recognition engine for bank cheque processing , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[37]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[38]  A. Kai Qin,et al.  Evolutionary extreme learning machine , 2005, Pattern Recognit..

[39]  Nei Kato,et al.  A Handwritten Character Recognition System Using Directional Element Feature and Asymmetric Mahalanobis Distance , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Chia-Feng Juang,et al.  Speech detection in noisy environments by wavelet energy-based recurrent neural fuzzy network , 2009, Expert Syst. Appl..

[41]  Gernot A. Fink,et al.  Markov models for offline handwriting recognition: a survey , 2009, International Journal on Document Analysis and Recognition (IJDAR).

[42]  Venu Govindaraju,et al.  OCR in a Hierarchical Feature Space , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Sreela Sasi,et al.  Handwritten character recognition using fuzzy logic, neuro-fuzzy, and wavelet transform approaches , 1997 .

[44]  Yuan Lan,et al.  Two-stage extreme learning machine for regression , 2010, Neurocomputing.

[45]  Jian-xiong Dong,et al.  Fast SVM training algorithm with decomposition on very large data sets , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  R. Venkatesh Babu,et al.  No-reference image quality assessment using modified extreme learning machine classifier , 2009, Appl. Soft Comput..

[47]  Mandava Rajeswari,et al.  Wavelet energy-guided level set-based active contour: A segmentation method to segment highly similar regions , 2010, Comput. Biol. Medicine.

[48]  Robert Sabourin,et al.  Leave-One-Out-Training and Leave-One-Out-Testing Hidden Markov Models for a Handwritten Numeral Recognizer: The Implications of a Single Classifier and Multiple Classifications , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Sargur N. Srihari,et al.  Offline Chinese handwriting recognition: an assessment of current technology , 2007, Frontiers of Computer Science in China.

[50]  Robert Sabourin,et al.  An optimized hill climbing algorithm for feature subset selection: evaluation on handwritten character recognition , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[51]  Bidyut Baran Chaudhuri,et al.  A Hybrid Scheme for Handprinted Numeral Recognition Based on a Self-Organizing Network and MLP Classifiers , 2002, Int. J. Pattern Recognit. Artif. Intell..

[52]  J. Tsukumo,et al.  Classification of handprinted Chinese characters using nonlinear normalization and correlation methods , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[53]  Chunheng Wang,et al.  Unconstrained handwritten character recognition based on WEDF and Multilayer Neural Network , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[54]  Yves Lecourtier,et al.  A structural/statistical feature based vector for handwritten character recognition , 1998, Pattern Recognit. Lett..

[55]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[56]  Hsin-Chia Fu,et al.  Multilinguistic handwritten character recognition by Bayesian decision-based neural networks , 1998, IEEE Trans. Signal Process..

[57]  Xiaoqing Ding,et al.  Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[58]  Yuan Lan,et al.  Constructive hidden nodes selection of extreme learning machine for regression , 2010, Neurocomputing.

[59]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[60]  Santanu Chaudhury,et al.  Bengali alpha-numeric character recognition using curvature features , 1993, Pattern Recognit..

[61]  Gyeonghwan Kim,et al.  Bankcheck Recognition Using Cross Validation Between Legal and Courtesy Amounts , 1997, Int. J. Pattern Recognit. Artif. Intell..

[62]  Bidyut Baran Chaudhuri,et al.  Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[63]  N. Shanthi,et al.  A novel SVM-based handwritten Tamil character recognition system , 2010, Pattern Analysis and Applications.

[64]  Robert K. L. Gay,et al.  Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning , 2009, IEEE Transactions on Neural Networks.

[65]  Adnan Amin,et al.  Hand-printed arabic character recognition system using an artificial network , 1996, Pattern Recognit..

[66]  G. W. Milligan,et al.  The validation of four ultrametric clustering algorithms , 1980, Pattern Recognit..

[67]  Venu Govindaraju,et al.  Offline Arabic handwriting recognition: a survey , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[68]  David Zhang,et al.  Wavelet Energy Feature Extraction and Matching for Palmprint Recognition , 2005, Journal of Computer Science and Technology.

[69]  R. J. Green,et al.  Recognition of Handwritten Cursive Arabic Characters , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[70]  Cheng-Lin Liu,et al.  Handwritten digit recognition: benchmarking of state-of-the-art techniques , 2003, Pattern Recognit..

[71]  João Marques de Carvalho,et al.  Optimizing the recognition rates of unconstrained handwritten numerals using biorthogonal spline wavelets , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[72]  G. Raju,et al.  Recognition of Unconstrained Handwritten Malayalam Characters Using Zero-crossing of Wavelet Coefficients , 2006, 2006 International Conference on Advanced Computing and Communications.

[73]  Xunkai Wei,et al.  Comparative Study of Extreme Learning Machine and Support Vector Machine , 2006, ISNN.

[74]  Cholwich Nattee,et al.  A Comprehensive Survey on On-line Handwriting Recognition Technology and its Real Application to the Nepalese Natural Handwriting , 2010 .

[75]  W. F. Clocksin,et al.  Structural Features of Cursive Arabic Script , 1999, BMVC.

[76]  Stavros J. Perantonis,et al.  Handwritten character recognition through two-stage foreground sub-sampling , 2010, Pattern Recognit..

[77]  Bidyut Baran Chaudhuri,et al.  Skew Angle Detection of Digitized Indian Script Documents , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[78]  Sunday O. Olatunji,et al.  Automatic Recognition of Off-line Handwritten Arabic (Indian) Numerals Using Support Vector and Extreme Learning Machines , 2009 .

[79]  Guang-Bin Huang,et al.  Convex Incremental Extreme Learning Machine , 2007 .

[80]  P. S. Satyanarayana,et al.  OCR for Script Identification of Hindi (Devnagari) Numerals using Error Diffusion Halftoning Algorithm with Neural Classifier , 2007 .