Abstract
Analyzing handwritten documents and detecting demographic attributes of writers from handwritten samples has received enormous attention from various fields of research, including psychology, computer science and artificial intelligence. Automatic detection of age, gender, handedness, nationality, and qualification of writers based on handwritten documents has several real-world applications, such as forensics and psychology. This paper proposes two simple but effective methods to detect the demographic information of writers from offline handwritten document images. The proposed methods are based on traditional and deep learning approaches. In the traditional machine learning method, the Rank Transform feature extraction method is used for measuring the intensity in handwriting images. The extracted handcrafted features are then fed into Support Vector Machine based classifiers to predict the demographical attributes of writers. In the deep learning method, a Convolutional Neural Network model based on the ResNet architecture with a fully connected layer, followed by a softmax layer is used to provide probability scores to facilitate demographic information detection. To evaluate the proposed methods and compare the results with the results in the literature, a comprehensive set of experiments was conducted on a frequently used benchmark database, KHATT. Both methods performed relatively well in predicting different demographic attributes. However, considering the settings in our experiments, the results obtained from the traditional model indicated better demographic detection compared to the deep learning models in all the tasks.
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References
Bouadjenek, N., Nemmour, H., Chibani, Y.: Histogram of Oriented Gradients for writer’s gender, handedness and age prediction. In: Symposium on Innovations in Intelligent Systems and Applications, pp.1–5 (2015). https://doi.org/10.1109/INISTA.2015.7276752
Navya, B.J., et al.: Multi-gradient directional features for gender identification. In: Proceedings of the International Conference on Pattern Recognition, pp.3657–3662 (2018). https://doi.org/10.1109/ICPR.2018.8546033
Navya, B.J., et al.: Adaptive multi-gradient kernels for handwritting based gender identification. In: Proceedings of the International Conference on Frontiers in Handwriting Recognition, pp.392–397 (2018). https://doi.org/10.1109/ICFHR-2018.2018.00075
AL-Qawasmeh, N., Suen, C.Y.: Gender detection from handwritten documents using concept of transfer-learning. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, W.-S., Cheriet, F., Suen, C.Y. (eds.) ICPRAI 2020. LNCS, vol. 12068, pp. 3–13. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59830-3_1
Moetesum, M., Siddiqi, I., Djeddi, C., Hannad, Y., Al-Maadeed, S.: Data driven feature extraction for gender classification using multi-script handwritten texts. In: Proceedings of the International Conference on Frontiers in Handwriting Recognition, pp. 564–569 (2018). https://doi.org/10.1109/ICFHR-2018.2018.00104
Illouz, E., (Omid) David, E., Netanyahu, N.S.: Handwriting-based gender classification using end-to-end deep neural networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 613–621. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_60
Al Maadeed, S., Hassaine, A.: Automatic prediction of age, gender, and nationality in offline handwriting. EURASIP J. Image Video Process. 2014(1), 1–10 (2014). https://doi.org/10.1186/1687-5281-2014-10
Alaei, F., Alaei, A.: Gender detection based on spatial pyramid matching. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12824, pp. 305–317. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86337-1_21
Morera, Á., Sánchez, Á., Vélez, J.F., Moreno, A.B.: Gender and handedness prediction from offline handwriting using convolutional neural networks. Complexity 2018 (2018)
Bouadjenek, N., Nemmour, H., Chibani, Y.: Age, gender and handedness prediction from handwriting using gradient features. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 1116–1120 (2015). https://doi.org/10.1109/ICDAR.2015.7333934
Basavaraja, V., Shivakumara, P., Guru, D.S., Pal, U., Lu, T., Blumenstein, M.: Age estimation using disconnectedness features in handwriting. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 1131–1136 (2019). https://doi.org/10.1109/ICDAR.2019.00183
Alaei, F., Alaei, A.: Handwriting analysis: Applications in person identification and forensic. In: Daimi, K., Francia, G., III., Encinas, L.H. (eds.) Breakthroughs in Digital Biometrics and Forensics, pp. 147–165. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10706-1_7
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994). https://doi.org/10.1007/BFb0028345
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Weston, J., Watkins, C.: Support vector machines for multi-class pattern recognition. Esann 99, 219–224 (1999)
He, K., Zhang, X. Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Pan, T.-S., Huang, H.-C., Lee, J.-C., Chen, C.-H.: Multi-scale ResNet for real-time underwater object detection. SIViP 15(5), 941–949 (2020). https://doi.org/10.1007/s11760-020-01818-w
Fan, Z., Liu, Y., Xia, V, Hou, J., Yan, F., Zang, Q.: ResAt-UNet: a U-shaped network using ResNet and attention module for image segmentation of urban buildings. Select. Topics Appl. Earth Observ. Remote Sens. 16, 1–20 (2023). https://doi.org/10.1109/JSTARS.2023.3238720
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. ArXiv Prepr. ArXiv13126120 (2013)
Mahmoud, S.A., et al.: KHATT: an open Arabic offline handwritten text database. Pattern Recognit. 47(3), 1096–1112 (2014)
Rabaev, I., Alkoran, I., Wattad, O., Litvak, M.: Automatic gender and age classification from offline handwriting with bilinear ResNet. Sensors 22(24), 9650 (2022). https://doi.org/10.3390/s22249650
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Alaei, F., Alaei, A. (2023). A Comparison of Demographic Attributes Detection from Handwriting Based on Traditional and Deep Learning Methods. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14194. Springer, Cham. https://doi.org/10.1007/978-3-031-41501-2_12
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