Skip to main content
SpringerLink
Log in

Feature Extraction Techniques for Gender Classification Based on Handwritten Text: A Critical Review

  • Conference paper
  • First Online:
Proceedings of International Conference on Frontiers in Computing and Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 404))

Abstract

Features of the handwritten text play a vital role in the area of handwriting identification. It became more challenging when one has to identify gender, age, and handedness of the person through handwriting. In last two decade, the use of various feature extraction techniques immerged having advantages one on the other. Ample research is done on writer identification systems by implementing various feature extraction techniques. In this paper, we have shifted the concern toward various features and features extraction techniques implemented on gender identification through handwriting. The objective of this survey is to present the critical review of work done in area feature extraction in gender identification taking only handwriting into consideration. We have categorized all the feature extraction techniques used by the researchers for gender classification into four broad categories: statistical-, transform-, gradient-, and model-based techniques. From the survey, we have identified few techniques that deserve future attention of the researchers for optimal results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Maken P, Gupta A (2021) A method for automatic classification of gender based on text- independent handwriting. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-10837-9

  2. Bouadjenek N, Nemmour H, Chibani Y (2016) Robust soft-biometrics prediction from off-line handwriting analysis. Appl Soft Comput 46:980–990

    Article  Google Scholar 

  3. Al Maadeed S, Hassaine A (2014) Automatic prediction of age, gender, and nationality in offline handwriting. Eurasip J Image Video Process, 1–10

    Google Scholar 

  4. Siddiqi I, Djeddi C, Raza A, Souici-meslati L (2015) Automatic analysis of handwriting for gender classification. Pattern Anal Appl 18(4):887–899. https://doi.org/10.1007/s10044-014-0371-0

    Article  MathSciNet  Google Scholar 

  5. Bi N, Suen CY, Nobile N, Tan J (2019) A multi-feature selection approach for gender identification of handwriting based on kernel mutual information. Pattern Recogn Lett 121:123–132. https://doi.org/10.1016/j.patrec.2018.05.005

    Article  Google Scholar 

  6. Gattal A, Djeddi C, Bensefia A, Ennaji A (2020) Handwriting based gender classification using COLD and Hinge features. In: Image and signal processing: 9th international conference, ICISP 2020, Marrakesh, Morocco, June 4–6, 2020, proceedings, 12119, 233–242. https://doi.org/10.1007/978-3-030-51935-3_25

  7. Ahmed M, Rasool AG, Afzal H, Siddiqi I (2017) Improving handwriting based gender classification using ensemble classifiers. Expert Syst Appl 85:158–168. https://doi.org/10.1016/j.eswa.2017.05.033

    Article  Google Scholar 

  8. Maji P, Chakraborty S, Samanta S, Chatterjee S, Kausar N, Dey N (2015) Effect of Euler number as a feature in gender recognition system from offline handwritten signature using neural networks. In: 2015 international conference on computing for sustainable global development, INDIA Com, 1869–1873

    Google Scholar 

  9. Dargan S, Kumar M (2021) Gender classification and writer identification system based on handwriting in Gurumukhi Script. In: 2021 international conference on computing, communication, and intelligent systems (ICCCIS). (ICCCIS), Greater Noida, India, pp 388–393. https://doi.org/10.1109/ICCCIS51004.2021.9397201

  10. Sokic E, Salihbegovic A, Ahic-Djokic M (2012) Analysis of off-line handwritten text samples of different gender using shape descriptors. In: 2012 9th international symposium on telecommunications, BIHTEL 2012 proceedings

    Google Scholar 

  11. Riza LS, Zainafif A, Rasim, & Nazir, S. (2018) Fuzzy rule-based classification systems for the gender prediction from handwriting. Telkomnika (Telecommun Comput Electron Control) 16(6):2725–2732

    Article  Google Scholar 

  12. Bouadjenek N, Nemmour H, Chibani Y (2017) Fuzzy integrals for combining multiple SVM and histogram features for writer’s gender prediction. IET Biometrics 6(6):429–437. https://doi.org/10.1049/iet-bmt.2016.0140

    Article  Google Scholar 

  13. Bouadjenek N, Nemmour H, Chibani Y (2014) Local descriptors to improve off-line handwriting-based gender prediction. In: 6th international conference on soft computing and pattern recognition, SoCPaR, 43–47

    Google Scholar 

  14. Bouadjenek N, Nemmour H, Chibani Y (2015) Age, gender and handedness prediction from handwriting using gradient features. In: 2015 13th international conference on document analysis and recognition (ICDAR), 1116–1120

    Google Scholar 

  15. Navya BJ, Shivakumara P, Shwetha GC, Roy S, Guru DS, Pal U, Lu T (2018) Adaptive multi-gradient kernels for handwritting based gender identification. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, 392–397. https://doi.org/10.1109/ICFHR-2018.2018.00075

  16. Navya BJ, Swetha GC, Shivakumara P, Roy S, Guru DS, Pal U, Lu T (2018) Multi-gradient directional features for gender identification. In: Proceedings—international conference on pattern recognition, 3657–3662

    Google Scholar 

  17. Gattal A, Djeddi C, Siddiqi I, Chibani Y (2018) Gender classification from offline multi-script handwriting images using oriented basic image features (oBIFs). Expert Syst Appl 99:155–167

    Google Scholar 

  18. Akbari Y, Nouri K, Sadri J, Djeddi C, Siddiqi I (2017) Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata. Image Vis Comput 59:17–30.https://doi.org/10.1016/j.imavis.2016.11.017

  19. Ibrahim AS, Youssef AE, Abbott AL (2015) Global vs. local features for gender identification using Arabic and English handwriting. In: 2014 IEEE international symposium on signal processing and information technology, ISSPIT, 155–160

    Google Scholar 

  20. Youssef AE, Ibrahim AS, Lynn Abbott A (2013) Automated gender identification for Arabic and English handwriting. In: 5th international conference on imaging for crime detection and prevention, ICDP. https://doi.org/10.1049/ic.2013.0274

  21. Guerbai Y, Chibani Y, Hadjadji B (2018) Handwriting gender recognition system based on the one-class support vector machines. In: Proceedings of the 7th international conference on image processing theory, tools and applications, IPTA 2017, 2018-Janua(4), 1–5. https://doi.org/10.1109/IPTA.2017.8310136

  22. Tan J, Bi N, Suen CY, Nobile N (2016) Multi-feature selection of handwriting for gender identification using mutual information. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, 0(Mi), 578–583

    Google Scholar 

  23. Mirza A, Moetesum M, Siddiqi I, Djeddi C (2016) Gender classification from offline handwriting images using textural features. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, pp 395–398. https://doi.org/10.1109/ICFHR.2016.0080

  24. Morera A, Sánchez v, Vélez JF, Moreno AB (2018) Gender and handedness prediction from offline handwriting using convolutional neural networks. Complexity https://doi.org/10.1155/2018/3891624

  25. Illouz E, David E, Netanyahu NS (2019) Handwriting-based gender classification using end-to-end deep neural networks. In arXiv (Vol. 1). Springer International Publishing. https://doi.org/10.1007/978-3-030-01424-7

  26. Moetesum M, Siddiqi I, Djeddi C, Hannad Y, Al-Maadeed S (2018) Data driven feature extraction for gender classification using multi-script handwritten texts. In: Proceedings of international conference on frontiers in handwriting recognition, ICFHR, 564–569.https://doi.org/10.1109/ICFHR-2018.2018.00104

  27. Xue G, Liu S, Gong D, Ma Y (2020) ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting. Neural Comput Appl 3. https://doi.org/10.1007/s00521-020-05237-3

  28. Rahmanian M, Shayegan MA (2021) Handwriting-based gender and handedness classification using convolutional neural networks. Multimedia Tools Appl. https://doi.org/10.1007/s11042-020-10170-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Goswami Ganesh Dutta Sanatan Dharma College, Chandigarh, India

    Monika Sethi

  2. Department of Computer Science and Applications, Panjab University Regional Centre, Muktsar, India

    M. K. Jindal

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

Authors
  1. Monika Sethi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. K. Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Subhadip Basu

  2. Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, India

    Dipak Kumar Kole

  3. Department of Information Technology, North-Eastern Hill University, Shillong, Meghalaya, India

    Arnab Kumar Maji

  4. Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland

    Dariusz Plewczynski

  5. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Debotosh Bhattacharjee

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sethi, M., Jindal, M.K., Kumar, M. (2023). Feature Extraction Techniques for Gender Classification Based on Handwritten Text: A Critical Review. In: Basu, S., Kole, D.K., Maji, A.K., Plewczynski, D., Bhattacharjee, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Lecture Notes in Networks and Systems, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-19-0105-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-0105-8_19

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0104-1

  • Online ISBN: 978-981-19-0105-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation