A Hybrid Mining Approach: Gender Prediction from Handwriting

  • Rahul Deo SahEmail author
  • Raja Ram Dutta
  • Asit Kumar Mohapatra
  • Neelamadhab Padhy
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


Although after many current technological achievements, there is still not any methodology which will allow a computer to perfectly copy the content of any complex handwritten document.

The handwriting recognition problem presents many difficulties as
  1. 1.

    The cursive nature of handwriting

  2. 2.

    Use of different pen types or the presence of paper with noisy background etc.

  3. 3.

    The individuality of handwriting has been studied and determined with specific precision.

There are two variants of the handwriting recognition problem
  1. a.


  2. b.



Online recognition problem deals with handwriting written with some electronic device that means it deals with real-world problems. whereas offline handwriting problem deals with handwriting which has been written previously and is kept stored. In the field of Human and Computer Interaction, if the gender of a user can automatically be predicted, just by using his or her signature, the system could offer him/her a more personalized interaction and care.

In spite of the fact that the facts confirm that females composing is alluring and neater than male one, this isn’t valid for every one of the cases. There are numerous precedents where we can discover ladylike appearance in manly penmanship. we intend to investigate the connections between the penmanship of various sexual orientations. To foresee the sexual orientation of people from examined pictures of their penmanship. This is finished by removing the arrangement of highlights from composing tests and preparing classifier to learn and separate between them. Highlights which is utilized for arrangement is tortuosity, shape, heading, chain code, and edge bearing. In this article, we mean to break down the connections between the penmanship of various sexual orientations. In this examination utilized, half breed classifiers are utilized and dissected their resultant. Furthermore, the resultant demonstrates the half and half classifiers give better outcomes.


Data mining Feature extraction Data cleaning Hybrid classification methods 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Rahul Deo Sah
    • 1
    Email author
  • Raja Ram Dutta
    • 2
  • Asit Kumar Mohapatra
    • 3
  • Neelamadhab Padhy
    • 4
  1. 1.Dr. Shyama Prasad Mukherjee UniversityRanchiIndia
  2. 2.Birla Institute of TechnologyDeogharIndia
  3. 3.Sri Satya Sai University of Technology and Medical SciencesPachamaIndia
  4. 4.GIET UniversityGunupurIndia

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