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TraitLWNet: a novel predictor of personality trait by analyzing Persian handwriting based on lightweight deep convolutional neural network

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Abstract

Based on psychologists’ theories, an individual’s handwriting somehow symbolizes a type of personality trait that can be a projection of the person’s innate. A person’s handwriting is the result of an organized system and has scientific bases that make it possible to analyze and specify individuals’ nature. This paper presents a novel real-time model based on handwriting samples collected from Persian-speaking people, which predicts their personality traits for the first time. Initially, 400 handwriting samples with a repetition of four different texts and psychological questionnaires and three psychologists’ comments have been collected. The pre-processing step is applied to the image samples and the decision-maker model was designed using a lightweight deep convolutional neural network (LWDCNN) structure. The texts were selected based on the psychologists’ guidance. The meaningful relation between the personality trait characters extracted from Persian handwriting and each of the personality traits of the person under-study is matched to a magnificent extent. Finally, the LWDCNN structure is evaluated based on the training samples. The proposed convolutional neural network provides reasonable accuracy for six different and three overlapping personality traits. Despite computational complexity and little time spent by the designed pre-train network to respond, the deep structure’s error level with limited layers is estimated smaller than 10%. The proposed algorithm’s efficiency has been proved by repeating the experiment and assessing measures such as accuracy and mean squared error (MSE).

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

    Maryam Saberi Anari

  2. Department of Biomedical Engineering, Meybod University, Meybod, Iran

    Khosro Rezaee

  3. Department of Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Ali Ahmadi

Authors
  1. Maryam Saberi Anari
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  2. Khosro Rezaee
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  3. Ali Ahmadi
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Contributions

M. Saber Anari and K. Rezaee conceived and planned the experiments. M. Saber Anari and A. Ahmadi carried out the experiments. M. Saber Anari and K. Rezaee planned and carried out the simulations. M. Saber Anari, K. Rezaee, and A. Ahmadi contributed to the interpretation of the results. M. Saber Anari and K. Rezaee took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript.

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Correspondence to Khosro Rezaee.

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Anari, M.S., Rezaee, K. & Ahmadi, A. TraitLWNet: a novel predictor of personality trait by analyzing Persian handwriting based on lightweight deep convolutional neural network. Multimed Tools Appl 81, 10673–10693 (2022). https://doi.org/10.1007/s11042-022-12295-3

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  • DOI: https://doi.org/10.1007/s11042-022-12295-3

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