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Automatic analysis of handwriting for gender classification

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Abstract

This paper presents a study to predict gender of individuals from scanned images of their handwritings. The proposed methodology is based on extracting a set of features from writing samples of male and female writers and training classifiers to learn to discriminate between the two. Writing attributes like slant, curvature, texture and legibility are estimated by computing local and global features. Classification is carried out using artificial neural networks and support vector machine. The proposed technique evaluated on two databases under a number of scenarios realized interesting results on predicting gender from handwriting.

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Authors and Affiliations

  1. Department of Computer Science, Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  2. LAMIS Laboratory, University of Tebessa, Tebessa, Algeria

    Chawki Djeddi

  3. Department of Computer Science, Badji Mokhtar-Annaba University, Annaba, Algeria

    Chawki Djeddi

  4. Medical Transcription Billing Corporation, Islamabad, Pakistan

    Ahsen Raza

  5. LISCO Laboratory, Badji Mokhtar-Annaba University, Annaba, Algeria

    Labiba Souici-meslati

Authors
  1. Imran Siddiqi
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  2. Chawki Djeddi
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  3. Ahsen Raza
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  4. Labiba Souici-meslati
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Correspondence to Imran Siddiqi.

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Siddiqi, I., Djeddi, C., Raza, A. et al. Automatic analysis of handwriting for gender classification. Pattern Anal Applic 18, 887–899 (2015). https://doi.org/10.1007/s10044-014-0371-0

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