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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Handwriting Word Recognition Based on SVM Classifier

Author 1: Mustafa S. Kadhm Author 2: Asst. Prof. Dr. Alia Karim Abdul Hassan
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 6, No. 11 · Published 2015 · Cited by 32

DOI: https://doi.org/10.14569/IJACSA.2015.061109

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Abstract

this paper proposed a new architecture for handwriting word recognition system Based on Support Vector Machine SVM Classifier. The proposed work depends on the handwriting word level, and it does not need for character segmentation stage. An Arabic handwriting dataset AHDB, dataset used for train and test the proposed system. Besides, the system achieved the best recognition accuracy 96.317% based on several feature extraction methods and SVM classifier. Experimental results show that the polynomial kernel of SVM is convergent and more accurate for recognition than other SVM kernels.

Keywords

Arabic Text Preprocessing Feature Extraction SVM

How to Cite this Article

Kadhm, M. S., & Hassan, A. P. D. A. K. A. (2015). Handwriting Word Recognition Based on SVM Classifier. International Journal of Advanced Computer Science and Applications, 6(11). https://doi.org/10.14569/IJACSA.2015.061109

Kadhm, Mustafa S., and Asst. Prof. Dr. Alia Karim Abdul Hassan. "Handwriting Word Recognition Based on SVM Classifier." International Journal of Advanced Computer Science and Applications, vol. 6, no. 11, 2015, https://doi.org/10.14569/IJACSA.2015.061109.

@article{Kadhm2015,
  title     = {Handwriting Word Recognition Based on SVM Classifier},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {11},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Mustafa S. Kadhm and Asst. Prof. Dr. Alia Karim Abdul Hassan},
  doi       = {10.14569/IJACSA.2015.061109},
  url       = {https://doi.org/10.14569/IJACSA.2015.061109}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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