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Gender Detection Based on Spatial Pyramid Matching

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12824))

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Abstract

The similarity and homogeneous visual appearance of male and female handwriting make gender detection from off-line handwritten document images a challenging research problem. In this paper, an effective method based on spatial pyramid matching is proposed for gender detection from handwritten document images. In the proposed method, the input handwritten document image is progressively divided into several sub-regions from coarse to fine levels. The weighted histograms of the sub-regions are then calculated. This process is resulting in a spatial pyramid feature set which is an extension of the orderless bag-of-features image representation. Classical classifiers, such as Support Vector Machines and ensemble classifiers, are considered for determining the gender (male and female) of individuals from their handwriting. Experiments were conducted on two benchmarks, QUWI and MSHD datasets, and the proposed method provided a promising improvement in gender detection accuracies, especially in script-dependent scenarios, compared with the results reported in the literature.

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Authors and Affiliations

  1. Southern Cross University, Gold Coast, Australia

    Fahimeh Alaei & Alireza Alaei

Authors
  1. Fahimeh Alaei
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  2. Alireza Alaei
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Correspondence to Fahimeh Alaei .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Alaei, F., Alaei, A. (2021). Gender Detection Based on Spatial Pyramid Matching. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-86337-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86336-4

  • Online ISBN: 978-3-030-86337-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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