Comparing Feature Matching Methods to Identify Persian Writers

Document Type : Original Article

Authors

1 Department of Computer Science, Ferdows Branch, Islamic Azad University Ferdows, Iran

2 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Islamic Azad University Ferdows Branch, Iran

Abstract

A variety of frequently utilized feature matching techniques are employed and integrated in this study to tackle the task of writer identification using Persian scripts. The literature demonstrates promise for feature matching methods in the writer identification field. Multiple methods, such as SIFT, SURF, BRISK, BRISK-SURF, FREAK, SURF-FREAK, SURF-BRISK, Harris-SURF, Harris-FREAK, and Harris-BRISK, were employed and their performance was analyzed and compared to establish the optimal method(s). The identification process involves the comparison of the query image with all reference images, and as such, the reference image with the highest number of matched feature points corresponds to the query image. To the best of our knowledge, no extensive study has compared the effectiveness of these methods in the context of identifying Persian writers. Among the methods compared, SIFT and SURF algorithms have demonstrated the highest accuracy in recognizing the correct writer, while other algorithms have exhibited encouraging results.

Keywords


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