DOI: https://doi.org/10.14710/jtsiskom.6.4.2018.129-134

Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

Jumoke Falilat Ajao  -  Department of Computer Science, Kwara State University, Nigeria
*David Olufemi Olawuyi  -  Department of Computer Science, Kwara State University, Nigeria
Odetunji Ode Odejobi  -  Department of Computer Engineering, Obafemi Awolowo University, Nigeria
Received: 23 Mar 2018; Accepted: 21 Apr 2018; Published: 31 Oct 2018.
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Abstract
This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.
Keywords: Yoruba characters recognition; Freeman chain code; K-Nearest Neighbor classification
Funding: Department of Computer Science, Kwara State University; Department of Computer Engineering, Obafemi Awolowo University

Article Metrics:

  1. S. Gunter and H. Bunke. “Ensembles of Classifiers for Handwritten Word Recognition,” International Journal of Document Analysis and Recognition, vol. 5, no. 4, pp. 224-232, 2003
  2. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. San Diego: Academic Press, 2009
  3. N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused on Off-Line Handwriting,” IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol. 31, no. 2, pp. 216-233, 2001
  4. J. F. Ajao, R. G. Jimoh, and S. O. Olabiyisi, “Handwritten Address Destination Recognition using Neural Networks,” Journal of Science, Technology, Mathematics and Education, vol. 9, no. 1, pp.70-91, 2012
  5. A. Ibraheem and O. Odejobi, “A System for the Recognition of Handwritten Yoruba Characters,” AGIS Ethiopia, Obafemi Awolowo University, Ile-Ife, Nigeria, 2011. [Online]. Available: http://www.slideshare.net/aflat/a-system-for-the-recognition-of-handwritten-yoruba-characters
  6. A. A. Desai, “Gujarati Handwritten Numeral Optical Character Reorganization through Neural Network,” Pattern Recognition, vol. 43, no. 7, pp. 2582–2589, 2010
  7. H. Bunke, “Recognition of Cursive Roman Handwriting: Past, Present and Future,” in Proc. of Seventh International Conference on Document Analysis and Recognition, Edinburgh, 2003, pp. 448–459
  8. C. Liu and C. Y. Suen, “A New Benchmark on the Recognition of Handwritten Bangla and Farsi Numeral Characters,” Pattern Recognition, vol. 42, no. 12, pp. 3287-3295, 2009
  9. D. Impedovo and G. Pirlo, “Zoning Methods for Handwritten Character Recognition: A Survey,” Pattern Recognition, Handwriting Recognition, and Other PR Applications, vol. 47, no. 3, pp. 969–981, 2014
  10. M. Cheriet, N. Kharma, C-L. Liu, and C. Y. Suen, Character Recognition Systems: A Guide for Students and Practitioners. New York: John Wiley, November 2007
  11. O. D. Femwa, “Development of a Writer-Independent Online, Handwritten Character Recognition System Using Modified Hybrid Neural Network Model,” PhD. Thesis, Ladoke Akintola University of Technology, Ogbomoso. 2012
  12. F. O. Deborah, O. E. Olusayo, and F. O. Alade, “Development of a Feature Extraction Technique for Online Character Recognition System,” Innovative Systems Design and Engineering, vol. 3, no. 3, pp. 10-23, 2012
  13. J. O. Adigun, O. D. Femwa, E. O. Omidiora, and S. O. Olabiyisi, “Optimized Features for Genetic Based Neural Network Model for Online Character Recognition,” British Journal of Mathematics & Computer Science, vol. 14, no. 6, pp.1-13, 2016
  14. W. Wang, “Optical Character Recognition, Using K-Nearest Neighbors,” arXiv:1411.1442 [cs.CV], Nov. 2014
  15. I. S. Shimasaan and E. M. Bukohwo, “An Artificial Network Model for Tiv Character Recognition,” Journal of Emerging trends in Computing and Information Sciences, vol. 6, no. 10, pp. 573-583, 2015
  16. R. Siddhartha and M. Saravanan. “Handwritten Character Recognition using KNN,” International Journal of Advance Research and Innovative Ideas in Education, vol. 3, no. 5, pp. 2395-4396, 2017
  17. W. L. Wang and M. H. Tang, “A Normalization Process to Standardize Handwriting Data Collected from Multiple Resources for Recognition,” Procedia Computer Science, vol. 61, pp. 402-409, 2015
  18. N. Azizi, N. Farah, and M. Sellami, “Off-line Handwritten Word Recognition System using Ensemble of Classifier Selection and Features Fusion,” Journal of Theoretical and Applied Information Technology, vol. 14, no. 2, pp. 141-150, 2010
  19. G. Katiyar, A. Katiyar, and S. Mehfuz, “Offline Handwritten Character Recognition System using Support Vector Machine,” American Journal of Neural Networks and Applications, vol. 3, no. 2, pp. 22-28, 2017
  20. M. Oladele, E. Omidiora, A. Temilola, and A. A. Sobowale, “An Offline Yoruba Handwritten Character Recognition System using Support Vector Machine,” International Conference of Sciences, Engineering & Environmental Technology (ICONSEET), vol. 2, no. 13, pp. 95-103, 2017
  21. J. F. Ajao, S. O. Olabiyisi, E. O. Omidiora, and O. O. Okediran, “Hidden Markov Model Approach for Offline Yoruba Handwritten Word Recognition,” British Journal of Mathematics & Computer Science, vol. 18, no. 6, pp. 1-20, 2016
  22. Omnigot, “The Yoruba Alphabets and Its Pronunciation,” [Online]. Available: http://www.omniglot.com/writing/yoruba.htm
  23. J. F. Ajao, S. O. Olabiyisi, O. O. Elijah, and O. O. Odetunji, “Yoruba Handwriting Word Recognition Quality Evaluation of Preprocessing Attributes using Information Theory Approach,” International Journal of Applied Information Systems, vol. 9, no. 1, pp. 18-23, 2015
  24. A. Bamgbose, Yoruba Orthography. Ibadan University Press, 1976
  25. H. J. Vala and A. Baxi, "A Review on Otsu Image Segmentation Algorithm," International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), vol. 2, no. 2, pp. 96-106, 2013
  26. E. Bribiesca, “A New Chain Code,” Pattern Recognition, vol. 32, no. 2, pp. 235–251, 1999

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