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Combination of Topological and Local Shape Features for Writer’s Gender, Handedness and Age Classification

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Image Analysis and Recognition (ICIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

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Abstract

In this work, writer’s gender, handedness and age range prediction is addressed through automatic analysis of handwritten sentences. Three SVM-based predictors associated to different data features are developed. Then, a Fuzzy MIN-MAX combination rule is proposed to aggregate robust prediction from individual systems. Experiments are carried on two public Arabic and English datasets. Results in terms of prediction accuracy demonstrate the usefulness of the proposed algorithm, which provides a gain between 1 % and 10 % over both individual systems and classical combination rules. Moreover, it is much more relevant than various state of the art methods.

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Notes

  1. 1.

    http://www.iam.unibe.ch/fki.

  2. 2.

    http://khatt.ideas2serve.net.

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Correspondence to Nesrine Bouadjenek .

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Bouadjenek, N., Nemmour, H., Chibani, Y. (2016). Combination of Topological and Local Shape Features for Writer’s Gender, Handedness and Age Classification. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_61

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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