A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral

Pakize Erdoğmuş; Abdullah Talha Kabakuş; Enver Küçükkülahlı; Büşra Takgil; Ezgi Kara Timuçin

doi:10.35377/saucis...1337649

Research Article

Year 2023, , 172 - 188, 31.12.2023

Pakize Erdoğmuş , Abdullah Talha Kabakuş , Enver Küçükkülahlı , Büşra Takgil , Ezgi Kara Timuçin

https://doi.org/10.35377/saucis...1337649

Abstract

References

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A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral

Year 2023, , 172 - 188, 31.12.2023

Pakize Erdoğmuş , Abdullah Talha Kabakuş , Enver Küçükkülahlı , Büşra Takgil , Ezgi Kara Timuçin

https://doi.org/10.35377/saucis...1337649

Abstract

Human handwriting is used to investigate human characteristics in various applications, including but not limited to biometric authentication, personality profiling, historical document analysis, and forensic investigations. Gender is one of the most distinguishing characteristics of human beings. From this point forth, we propose a novel end-to-end model based on Convolutional Neural Network (CNN) that automatically extracts features from a given handwritten sample, which contains both handwritten text and numerals unlike the related work that uses only handwritten text, and classifies its owner’s gender. In addition to proposing a novel model, we introduce a new dataset that consists of 530 gender-labeled Turkish handwritten samples since, to the best of our knowledge, there does not exist a public gender-labeled Turkish handwriting dataset. Following an exhaustive process of hyperparameter optimization, the proposed CNN featured the most optimal hyperparameters and was both trained and evaluated on this dataset. According to the experimental result, the proposed novel model obtained an accuracy as high as 74.46%, which overperformed the state-of-the-art baselines and is promising on such a task that even humans could not have achieved highly-accurate results for, as of yet.

Keywords

handwriting, gender classification, convolutional neural network, computer vision, forensic science

References

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[28] Y. Akbari, K. Nouri, J. Sadri, C. Djeddi, and I. Siddiqi, “Wavelet-based gender detection on off-line handwritten documents using probabilistic finite state automata,” Image Vis Comput, vol. 59, no. C, pp. 17–30, 2017, doi: 10.1016/j.imavis.2016.11.017.
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[31] P. Maken and A. Gupta, “A method for automatic classification of gender based on text- independent handwriting,” Multimed Tools Appl, vol. 80, no. 16, pp. 24573–24602, Jul. 2021, doi: 10.1007/s11042-021-10837-9.
[32] G. Xue, S. Liu, D. Gong, and Y. Ma, “ATP-DenseNet: a hybrid deep learning-based gender identification of handwriting,” Neural Comput Appl, vol. 33, pp. 4611–4622, May 2021, doi: 10.1007/s00521-020-05237-3.
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Details

Primary Language	English
Subjects	Computer Software
Journal Section	Articles
Authors	Pakize Erdoğmuş 0000-0003-2172-5767 Abdullah Talha Kabakuş 0000-0003-2181-4292 Enver Küçükkülahlı 0000-0002-0525-0477 Büşra Takgil 0000-0002-7927-0083 Ezgi Kara Timuçin 0000-0003-1596-3427
Early Pub Date	December 27, 2023
Publication Date	December 31, 2023
Submission Date	August 4, 2023
Acceptance Date	September 28, 2023
Published in Issue	Year 2023

Cite

IEEE	P. Erdoğmuş, A. T. Kabakuş, E. Küçükkülahlı, B. Takgil, and E. Kara Timuçin, “A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral”, SAUCIS, vol. 6, no. 3, pp. 172–188, 2023, doi: 10.35377/saucis...1337649.

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