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Effects of artificially intelligent tools on pattern recognition

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Abstract

Pattern recognition is classification process that attempts to assign each input value to one of a given set of classes. The process of pattern recognition in the state of art has been achieved either by training of artificially intelligent tools or using heuristic rule based approaches. The objective of this paper is to provide a comparative study between artificially trained and heuristics rule based techniques employed for pattern recognition in the state of the art focused on script pattern recognition. It is observed that mainly there are two categories of script pattern recognition techniques. First category involves assistance of artificial intelligent learning and next, is based on heuristic-rules for cursive script pattern segmentation/recognition. Accordingly, a detailed critical study is performed that focuses on size of training/testing data and implication of artificial learning on script pattern recognition accuracy. Moreover, the techniques are described in details that are employed to identify character patterns. Finally, performances of different techniques on benchmark database are compared regarding pattern recognition accuracy, error rate, single or multiple classifiers being employed. Problems that still persist are also highlighted and possible directions are set.

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Acknowledgments

This research work is partially supported by TWAS fellowship cycle (2010) and Higher Education Commission of Pakistan. The authors are sincerely thankful to Prof. Dr. Ghazali Sulong and Prof. Dr. Dzulkifli Mohammad Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM) Skudai Johor Malaysia for support, guidance and layout of this research.

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  1. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johor, Malaysia

    Tanzila Saba & Amjad Rehman

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  1. Tanzila Saba
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  2. Amjad Rehman
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Correspondence to Tanzila Saba.

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Saba, T., Rehman, A. Effects of artificially intelligent tools on pattern recognition. Int. J. Mach. Learn. & Cyber. 4, 155–162 (2013). https://doi.org/10.1007/s13042-012-0082-z

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