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World Scientific Publishing, International Journal on Artificial Intelligence Tools, 03(25), p. 1650015

DOI: 10.1142/s0218213016500159

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Skeleton Hinge Distribution for Writer Identification

Journal article published in 2016 by Paraskevas Diamantatos, Ergina Kavallieratou, Stefanos Gritzalis
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

In this paper, a feature that is based on statistical directional features is presented. Specifically, an improvement of the statistical feature: edge hinge distribution, is attempted. Furthermore, different matching techniques are applied. For the evaluation, the Firemaker DB was used, which consists of samples from 250 writers, including 4 pages per writer. The suggested feature, the skeleton hinge distribution, achieved accuracy of 90.8% using nearest neighbor with Manhattan distance for matching.