“School of Cognitive Sciences”
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| Paper IPM / Cognitive Sciences / 9583 |
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| Abstract: | |||||
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Previous studies have shown the advantages of Gabor filters for the purpose of writer identification. Current study attempts to show that designing a set of such filters is domain specific however tuning of their parameters is not an easy task. Here we use genetic algorithms as a systematic approach to search in the space of filter parameters to choose a set of best filters for the problem of offline writer identification based on handwriting. Specifically, each chromosome encodes the parameters of filters and then during an evolutionary process those filters which show the best classification performance are selected. Identification was performed using two different classifiers: weighted Euclidean distance (WED) and nearest neighbor classifier (KNN). Our results show the superiority of proposed method compared with traditional Gabor filters and Gray Scale Co-occurrence matrices (GSCM).
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