A New Radial Basis Function Artificial Neural Network based Recognition for Kurdish Manuscript

During recent decades, recognizing letters was a considerable discussion for artificial intelligence researchers and recognize letters due to the variety of languages and different approaches have many challenges. The Artificial Neural Networks (ANNs) are framed based on particular application such as recognition pattern and data classification through learning process is configured. So, it is a proper approach to recognize letters. Kurdish language has two popular handwritings based on Arabic and Latin. In this paper, Radial Basis Function (RBF) of ANNs is used to recognize Kurdish-Latin manuscripts. Although, the authors' proposed method is also used to recognize the letters of all Latin languages which include English, Turkish and etc. are used. The authors implement RBF of ANNs in MATLAB environment. In this paper, the efficiency criteria is supposed to minimize the Mean Square Error (MSE) to recognize Kurdish letters and maximize recognition accuracy of Kurdish letters in training and testing stage of RBF of ANNs. The recognition accuracy in training and testing stages are 100% and 96.7742%, respectively.

[1]  Valeri M. Mladenov,et al.  Speech recognition using neural networks , 2010, 10th Symposium on Neural Network Applications in Electrical Engineering.

[2]  C. Sureshkumar.  Handwritten Tamil Character Recognition and Conversion using Neural Network , 2010 .

[3]  Wei-Chiang Samuelson Hong.  Principal Concepts in Applied Evolutionary Computation: Emerging Trends , 2012 .

[4]  Karl Sims,et al.  Handwritten Character Classification Using Nearest Neighbor in Large Databases , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Abraham Kandel,et al.  Entropy, and the recognition of fuzzy letters , 1989 .

[6]  Farhad Soleimanian Gharehchopogh.  Neural networks application in software cost estimation: A case study , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

[7]  E. G. Rajan,et al.  Writer Identification and Recognition Using Radial Basis Function , 2010 .

[8]  Brijesh Verma,et al.  Handwritten Hindi character recognition using multilayer perceptron and radial basis function neural networks , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  D. S. Yeung,et al.  A neural network recognition system for handwritten Chinese character using structured approach , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

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

[11]  Satchidananda Dehuri,et al.  MVClustViz: A Novice Yet Simple Multivariate Cluster Visualization Technique for Centroid-based Clusters , 2013, Int. J. Syst. Dyn. Appl..

[12]  Nicoletta Sala,et al.  Complexity Science, Living Systems, and Reflexing Interfaces: New Models and Perspectives , 2012 .

[13]  Farhad Soleimanian Gharehchopogh,et al.  Artificial Neural Network Application In Letters Recognition For Farsi/Arabic Manuscripts , 2012 .

[14]  T. Sitamahalakshmi,et al.  Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition , 2011 .

[15]  Mohammed Obaid Mustafa,et al.  International Journal of System Dynamics Applications , 2014 .

[16]  Fevzullah Temurtas,et al.  Chest diseases diagnosis using artificial neural networks , 2010, Expert Syst. Appl..

[17]  Sibanda Wilbert,et al.  Novel Application of Multi-Layer Perceptrons (MLP) Neural Networks to Model HIV in South Africa using Seroprevalence Data from Antenatal Clinics , 2011 .

[18]  Farhad Soleimanian Gharehchopogh,et al.  Security Challenges In Cloud Computing With More Emphasis on Trust And Privacy , 2012 .

[19]  Jung-Hsien Chiang,et al.  A hybrid neural network model in handwritten word recognition , 1998, Neural Networks.

[20]  Rashnodi Omid,et al.  Using Box Approach in Persian Handwritten Digits Recognition , 2011 .

[21]  Nicoletta Sala,et al.  Reflexing Interfaces: The Complex Coevolution of Information Technology Ecosystems , 2008 .

[22]  K. Yamada,et al.  Handwritten numeral recognition by multilayered neural network with improved learning algorithm , 1989, International 1989 Joint Conference on Neural Networks.

[23]  Ibrahiem M. M. El Emary,et al.  Probabilistic Artificial Neural Network For Recognizing the Arabic Hand Written Characters , 2006 .

[24]  Vincenzo De Florio,et al.  Innovations and Approaches for Resilient and Adaptive Systems , 2012 .

[26]  Marisela Rodríguez Salvador,et al.  Dynamic Modeling in New Product Development: The Case of Knowledge Management Enablers in a Food Product , 2014, Int. J. Syst. Dyn. Appl..

[27]  Eleonora Bilotta,et al.  Biological Traits in Artificial Self-Reproducing Systems , 2012, Int. J. Signs Semiot. Syst..

[28]  Anas M. Bashayreh.  Organizational Culture and Effect on Organizational Performance: Study on Jordanian Insurance Sector , 2014, Int. J. Knowl. Syst. Sci..

[29]  Yoshiteru Nakamori,et al.  Clarification of Abilities and Qualities of Knowledge Coordinators: The Case of Regional Revitalization Projects , 2011, Int. J. Knowl. Syst. Sci..

[30]  Jiann-Liang Chen,et al.  IoT-IMS Communication Platform for Future Internet , 2011, Int. J. Adapt. Resilient Auton. Syst..

[31]  P. Morasso,et al.  Neural models of cursive script handwriting , 1989, International 1989 Joint Conference on Neural Networks.

[32]  Rashnodi Omid,et al.  Persian Handwritten Digit Recognition using Support Vector Machines , 2011 .

[33]  Reza Gharoie Ahangar,et al.  Handwritten Farsi Character Recognition using Artificial Neural Network , 2009, ArXiv.

[34]  Hussein Almuallim,et al.  A Method of Recognition of Arabic Cursive Handwriting , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.