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

EN

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

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characteristics in various applications, including but not limited to biometric authentication, personality profiling, historical document analysis, and forensic investigations.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

References

[1] J. Wayman, A. Jain, D. Maltoni, and D. Maio, Biometric Systems, 1st ed. London: Springer London, 2005. doi: 10.1007/b138151.
[2] R. N. King and D. J. Koehler, “Illusory correlations in graphological inference,” J Exp Psychol Appl, vol. 6, no. 4, pp. 336–336, 2000, doi: 10.1037/1076-898X.6.4.336.
[3] V. Shackleton and S. Newell, “European Management Selection Methods: A Comparison of Five Countries,” International Journal of Selection and Assessment, vol. 2, no. 2, pp. 91–102, 1994, doi: 10.1111/j.1468-2389.1994.tb00155.x.
[4] M. Ahmed, A. G. Rasool, H. Afzal, and I. Siddiqi, “Improving handwriting based gender classification using ensemble classifiers,” Expert Syst Appl, vol. 85, pp. 158–168, 2017, doi: 10.1016/j.eswa.2017.05.033.
[5] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Local Descriptors to Improve Off-line Handwriting-based Gender Prediction,” in Proceedings of the 2014 6th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2014), Tunis, Tunisia: IEEE, pp. 43–47, 2014. doi: 10.1109/SOCPAR.2014.7007979.
[6] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Age, Gender and Handedness Prediction from Handwriting using Gradient Features,” in Proceedings of the 2015 13th International Conference on Document Analysis and Recognition (ICDAR ’15), Washington, DC, United States: IEEE, 2015, pp. 1116–1120. doi: 10.1109/ICDAR.2015.7333934.
[7] “What is gender? What is sex?,” Canadian Institutes of Health Research, 2020 [Online]. Available: https://cihr-irsc.gc.ca/e/48642.html. [Accessed: 10-Aug-2022].
[8] L. A. M. Galea and D. Kimura, “Sex differences in route-learning,” Pers Individ Dif, vol. 14, no. 1, pp. 53–65, 1993, doi: 10.1016/0191-8869(93)90174-2.

[9] E. Coluccia, G. Iosue, and M. Antonella Brandimonte, “The relationship between map drawing and spatial orientation abilities: A study of gender differences,” J Environ Psychol, vol. 27, no. 2, pp. 135–144, 2007, doi: 10.1016/j.jenvp.2006.12.005.
[10] R. S. Astur, A. J. Purton, M. J. Zaniewski, J. Cimadevilla, and E. J. Markus, “Human sex differences in solving a virtual navigation problem,” Behavioural Brain Research, vol. 308, pp. 236–243, 2016, doi: 10.1016/j.bbr.2016.04.037.
[11] D. A. Cahn-Weiner, K. Williams, J. Grace, G. Tremont, H. Westervelt, and R. A. Stern, “Discrimination of Dementia with Lewy bodies from Alzheimer disease and Parkinson disease using the Clock Drawing Test,” Cognitive and Behavioral Neurology, vol. 16, no. 2, pp. 85–92, 2003, doi: 10.1097/00146965-200306000-00001.
[12] K. Amunts et al., “Gender-specific left-right asymmetries in human visual cortex,” Journal of Neuroscience, vol. 27, no. 6, pp. 1356–1364, 2007, doi: 10.1523/JNEUROSCI.4753-06.2007.
[13] A. C. Hurlbert and Y. Ling, “Biological components of sex differences in color preference,” Current Biology, vol. 17, no. 16, pp. 623–625, 2007, doi: 10.1016/j.cub.2007.06.022.
[14] S. Bradley, “Handwriting and Gender: A multi-use data set,” Journal of Statistics Education, vol. 23, no. 1, pp. 1–15, 2015, doi: 10.1080/10691898.2015.11889721.
[15] G. Cordasco, M. Buonanno, M. Faundez-Zanuy, M. T. Riviello, L. Likforman-Sulem, and A. Esposito, “Gender Identification through Handwriting: an Online Approach,” in Proceedings of the 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2020), Mariehamn, Finland: IEEE, pp. 197–202, 2020. doi: 10.1109/CogInfoCom50765.2020.9237863.
[16] S. Saha, M. Asif, B. Khaled, M. S. Islam, N. Saha Puja, and M. Hasan, “Detecting Sex From Handwritten Examples; Detecting Sex From Handwritten Examples,” in 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India: IEEE, pp. 1–7, 2018.
[17] S. Upadhyay, J. Singh, and S. K. Shukla, “Determination of Sex Through Handwriting Characteristics,” Int J Curr Res Rev, vol. 9, no. 13, pp. 11–18, 2017, doi: 10.7324/ijcrr.2017.9133.
[18] E. Illouz, E. Omid David, and N. S. Netanyahu, “Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks,” in Proceedings of the 27th International Conference on Artificial Neural Networks (ICANN 2018), Island of Rhodes, Greece: Springer Verlag, pp. 613–621, 2018. doi: 10.1007/978-3-030-01424-7_60.
[19] M. Koppel, S. Argamon, and A. R. Shimoni, “Automatically Categorizing Written Texts by Author Gender,” Literary and Linguistic Computing, vol. 17, no. 4, pp. 1–13, 2002, doi: 10.1093/llc/17.4.401.
[20] M. Liwicki, A. Schlapbach, P. Loretan, and H. Bunke, “Automatic Detection of Gender and Handedness from On-Line Handwriting,” in Proceedings of the 13th Biennial Conference of the International Graphonomics Society (IGS2007), Melbourne, Australia, pp. 179–183, 2007.
[21] A. Gattal, C. Djeddi, A. Bensefia, and A. Ennaji, “Handwriting Based Gender Classification Using COLD and Hinge Features,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, pp. 233–242, 2020. doi: 10.1007/978-3-030-51935-3_25.
[22] Á. Morera, Á. Sánchez, J. F. Vélez, and A. B. Moreno, “Gender and Handedness Prediction from Offline Handwriting Using Convolutional Neural Networks,” Complexity, vol. 2018, pp. 1–14, 2018, doi: 10.1155/2018/3891624.
[23] I. Rabaev, M. Litvak, S. Asulin, and O. H. Tabibi, “Automatic Gender Classification from Handwritten Images: A Case Study,” in Proceedings of the 19th International Conference on Computer Analysis of Images and Patterns (CAIP 2021), Virtual, 2021, pp. 329–339, 2021. doi: 10.1007/978-3-030-89131-2_30.
[24] J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proceeding of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, FL, USA: IEEE, pp. 248–255, 2009. doi: 10.1109/cvpr.2009.5206848.
[25] S. al Maadeed and A. Hassaine, “Automatic prediction of age, gender, and nationality in offline handwriting,” EURASIP J Image Video Process, vol. 2014, no. 10, pp. 1–10, 2014, doi: 10.1186/1687-5281-2014-10.
[26] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Histogram of Oriented Gradients for Writer’s Gender, Handedness and Age prediction,” in Proceedings of the 2015 International Symposium on Innovations in Intelligent SysTems and Applications, Proceedings (INISTA 2015), pp. 1–5, 2015. doi: 10.1109/INISTA.2015.7276752.
[27] I. Siddiqi, C. Djeddi, A. Raza, and L. Souici-meslati, “Automatic analysis of handwriting for gender classification,” Pattern Analysis and Applications, vol. 18, pp. 887–899, 2015, doi: 10.1007/s10044-014-0371-0.
[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.
[29] N. Bouadjenek, H. Nemmour, and Y. Chibani, “Writer’s Gender Classification Using HOG and LBP Features,” in Proceedings of the International Conference on Electrical Engineering and Control Applications (ICEECA 2016), Kuala Lumpur, Malaysia, pp. 1–5, 2016. doi: 10.1007/978-3-319-48929-2_24.
[30] A. E. Youssef, A. S. Ibrahim, and A. Lynn Abbott, “Automated Gender Identification for Arabic and English Handwriting,” in Proceedings of the 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013), London, UK, pp. 1–6. Doi, 2013: 10.1049/ic.2013.0274.
[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.
[33] E. Belval, “pdf2image: A python (3.6+) module that wraps pdftoppm and pdftocairo to convert PDF to a PIL Image object,” 2021. [Online]. Available: https://github.com/Belval/pdf2image [Accessed: 10-Aug-2022].
[34] G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, vol. 120, pp. 122–125, 2000.
[35] The pandas development team, “pandas: Python Data Analysis Library,” 2020. [Online]. Available: https://pandas.pydata.org. [Accessed: 12-Jul-2022].
[36] W. McKinney, “Data Structures for Statistical Computing in Python,” in Proceedings of the 9th Python in Science Conference (SCIPY 2010), Austin, Texas, pp. 56–61, 2010. doi: 10.25080/majora-92bf1922-00a.
[37] F. Chollet, “Keras: the Python deep learning API,” 2015. [Online]. Available: https://keras. [Accessed: 2-Dec-2022].
[38] M. Abadi et al., “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), Savannah, GA, USA, pp. 265–283, 2016.
[39] F. Chollet, Deep Learning with Python. Manning Publications, 2017.
[40] C. R. Harris et al., “Array Programming with NumPy,” Nature, vol. 585, pp. 357–362, 2020, doi: 10.1038/s41586-020-2649-2.
[41] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[42] J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Comput Sci Eng, vol. 9, no. 3, pp. 90–95, 2007, doi: 10.1109/MCSE.2007.55.
[43] “CS231n Convolutional Neural Networks for Visual Recognition,” Stanford University, 2020. https://cs231n.github.io/convolutional-networks (accessed Dec. 02, 2022).
[44] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.
[45] V. Christlein, L. Spranger, M. Seuret, A. Nicolaou, P. Král, and A. Maier, “Deep Generalized Max Pooling,” in Proceedings of the 15th International Conference on Document Analysis and Recognition (ICDAR 2019), Sydney, Australia, pp. 1–7, 2019.
[46] V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings, of the 27th International Conference on Machine Learning (ICML 2010), Madison, WI, USA, pp. 807–814, 2010.
[47] A. Mollahosseini, D. Chan, and M. H. Mahoor, “Going deeper in facial expression recognition using deep neural networks,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV 2016), Lake Placid, NY, USA: IEEE, pp. 1–10, 2016. doi: 10.1109/WACV.2016.7477450.
[48] D. C. Johny et al., “ADADELTA: An Adaptive Learning Rate Method,” IEEE Access, vol. 7, no. November, 2018.
[49] X. Zhang, X. Chen, L. Yao, C. Ge, and M. Dong, “Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning,” in International Conference on Neural Information Processing (ICONIP 2019), Sydney, NSW, Australia: Springer, pp. 287–295, 2019. doi: 10.1007/978-3-030-36808-1_31.
[50] L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar, “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization,” Journal of Machine Learning Research, vol. 18, no. 1, pp. 6765–6816, 2018.
[51] D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” in Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, California, USA, pp. 1–15, 2015.
[52] G. Hinton, N. Srivastava, and K. Swersky, “Neural Networks for Machine Learning,” 2012.
[53] H. Robbins and S. Monro, “A Stochastic Approximation Method,” The Annals of Mathematical Statistics, vol. 22, no. 3, pp. 400–407, 1951, doi: 10.1214/aoms/1177729586.
[54] K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” in Proceedings of the 14th European Conference on Computer Vision (ECCV 2016), Amsterdam, The Netherlands, Oct. 2016. doi: 10.1007/978-3-319-46493-0_38.
[55] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2016), Seattle, Washington, USA: IEEE, pp. 2818–2826,2016. doi: 10.1109/CVPR.2016.308.
[56] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, USA: IEEE, pp. 4510–4520, 2018. doi: 10.1109/CVPR.2018.00474.

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Pakize Erdoğmuş ^*
0000-0003-2172-5767
Türkiye

Abdullah Talha Kabakuş
0000-0003-2181-4292
Türkiye

Enver Küçükkülahlı
0000-0002-0525-0477
Türkiye

Büşra Takgil
0000-0002-7927-0083
Türkiye

Ezgi Kara Timuçin
0000-0003-1596-3427
Türkiye

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 1970 Volume: 6 Number: 3

DOI

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

IZ

https://izlik.org/JA78XJ24EP

Cite

RIS / Bibtex

APA

Erdoğmuş, P., Kabakuş, A. T., Küçükkülahlı, E., Takgil, B., & Kara Timuçin, E. (2023). A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral. Sakarya University Journal of Computer and Information Sciences, 6(3), 172-188. https://doi.org/10.35377/saucis...1337649

AMA

1.Erdoğmuş P, Kabakuş AT, Küçükkülahlı E, Takgil B, Kara Timuçin E. A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral. SAUCIS. 2023;6(3):172-188. doi:10.35377/saucis.1337649

Chicago

Erdoğmuş, Pakize, Abdullah Talha Kabakuş, Enver Küçükkülahlı, Büşra Takgil, and Ezgi Kara Timuçin. 2023. “A Novel Gender Classification Model Based on Convolutional Neural Network through Handwritten Text and Numeral”. Sakarya University Journal of Computer and Information Sciences 6 (3): 172-88. https://doi.org/10.35377/saucis. 1337649.

EndNote

Erdoğmuş P, Kabakuş AT, Küçükkülahlı E, Takgil B, Kara Timuçin E (December 1, 2023) A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral. Sakarya University Journal of Computer and Information Sciences 6 3 172–188.

IEEE

[1]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, Dec. 2023, doi: 10.35377/saucis...1337649.

ISNAD

Erdoğmuş, Pakize - Kabakuş, Abdullah Talha - Küçükkülahlı, Enver - Takgil, Büşra - Kara Timuçin, Ezgi. “A Novel Gender Classification Model Based on Convolutional Neural Network through Handwritten Text and Numeral”. Sakarya University Journal of Computer and Information Sciences 6/3 (December 1, 2023): 172-188. https://doi.org/10.35377/saucis. 1337649.

JAMA

1.Erdoğmuş P, Kabakuş AT, Küçükkülahlı E, Takgil B, Kara Timuçin E. A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral. SAUCIS. 2023;6:172–188.

MLA

Erdoğmuş, Pakize, et al. “A Novel Gender Classification Model Based on Convolutional Neural Network through Handwritten Text and Numeral”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 3, Dec. 2023, pp. 172-88, doi:10.35377/saucis. 1337649.

Vancouver

1.Pakize Erdoğmuş, Abdullah Talha Kabakuş, Enver Küçükkülahlı, Büşra Takgil, Ezgi Kara Timuçin. A Novel Gender Classification Model based on Convolutional Neural Network through Handwritten Text and Numeral. SAUCIS. 2023 Dec. 1;6(3):172-88. doi:10.35377/saucis. 1337649