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International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 2015: Machine Learning and Data Mining in Pattern Recognition pp 342–354Cite as

Offline Writer Identification in Tamil Using Bagged Classification Trees

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9166))

Abstract

In this paper, we explore the effectiveness of bagged classification trees, in solving the writer identification problem in the Tamil language. Unlike other languages, in Tamil the writer identification problem is mostly an unexplored problem. Novel feature extraction methods tailored to better understand Tamil characters have been proposed. The feature extraction methods used in this paper are chosen after analysing the statistical spread of a feature across different handwriting classes. We have also analysed how increasing the number of bagged classification trees would affect the classification accuracy. Our learning algorithm is trained with hundred and forty four samples and is tested with twenty different samples per handwriting style. In total the algorithm is trained with ten different handwriting styles. Using the proposed features and bagged classification trees, we achieve 76.4 % accuracy. The practicality of the proposed method is also analysed using a few time consumption measuring parameters.

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Author information

Authors and Affiliations

  1. SVCE, Anna University, Chennai, Tamil Nadu, India

    Sudarshan Babu

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  1. Sudarshan Babu
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Correspondence to Sudarshan Babu .

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Editors and Affiliations

  1. IBaI, Leipzig, Germany

    Petra Perner

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© 2015 Springer International Publishing Switzerland

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Babu, S. (2015). Offline Writer Identification in Tamil Using Bagged Classification Trees . In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science(), vol 9166. Springer, Cham. https://doi.org/10.1007/978-3-319-21024-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-21024-7_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21023-0

  • Online ISBN: 978-3-319-21024-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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