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Writer age estimation through handwriting

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Abstract

Handwritten image-based writer age estimation is a challenging task due to the various writing styles of different individuals, use of different scripts, varying alignment, etc. Unlike age estimation using face recognition in biometrics, handwriting-based age classification is reliable and inexpensive because of the plain backgrounds of documents. This paper presents a novel model for deriving the phase spectrum based on the Harmonic Wavelet Transform (HWT) for age classification on handwritten images from 11 to 65 years. This includes 11 classes with an interval of 5 years. In contrast to the Fourier transform, which provides a noisy phase spectrum due to loss of time variations, the proposed HWT-based phase spectrum retains time variations of phase and magnitude. As a result, the proposed HWT-based phase spectrum preserves vital information of changes in handwritten images. In order to extract such information, we propose new phase statistics-based features for age classification based on the understanding that as age changes, writing style also changes. The features and the input images are fed to a VGG-16 model for age classification. The proposed method is tested on our own dataset and three standard datasets, namely, IAM-2, KHATT and that of Basavaraja et al. to demonstrate the effectiveness of the proposed model compared to the existing methods in terms of classification rate. The results of the proposed and existing methods on different datasets show that the proposed method outperforms the existing methods in terms of classification rate.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This work is supported by the Natural Science Foundation of China under Grant 61,672,273 and Grant 61,832,008, and Ministry of Higher Education of Malaysia for the generous grant Fundamental Research Grant Scheme (FRGS) with code number FRGS/1/2020/ICT02/UM/02/4.

The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.

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Authors and Affiliations

  1. National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

    Zhiheng Huang & Tong Lu

  2. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Palaiahnakote Shivakumara & Maryam Asadzadeh Kaljahi

  3. Dhirubhai Ambani Institute of Information and Communication Technology, Ahmedabad, Gujarat, India

    Ahlad Kumar

  4. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Umapada Pal

  5. Australian Artificial Intelligence Institute, University Technology Sydney, Sydney, NSW, Australia

    Michael Blumenstein

Authors
  1. Zhiheng Huang
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  2. Palaiahnakote Shivakumara
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  3. Maryam Asadzadeh Kaljahi
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  4. Ahlad Kumar
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  5. Umapada Pal
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  6. Tong Lu
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  7. Michael Blumenstein
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Correspondence to Tong Lu.

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Huang, Z., Shivakumara, P., Kaljahi, M.A. et al. Writer age estimation through handwriting. Multimed Tools Appl 82, 16033–16055 (2023). https://doi.org/10.1007/s11042-022-13840-w

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  • DOI: https://doi.org/10.1007/s11042-022-13840-w

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