Review of Signature Recognition Using Deep Learning with Dataset Profiling for Career Pathway Recommendation

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Publication Details

Journal
International Journal of Sustainable Construction Engineering and Technology (Universiti Tun Hussein Onn Malaysia)
Published
2026-09-22
Primary Topic
Personality Traits and Psychology
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

Review of Signature Recognition Using Deep Learning with Dataset Profiling for Career Pathway Recommendation

Aspalaila Abdullah, Siti Zarina Mohd Muji, Muhammad Sazlan Abdul Kadar, Athirah Batrisya Ramli et al.
International Journal of Sustainable Construction Engineering and Technology (Universiti Tun Hussein Onn Malaysia)
Personality Traits and Psychology
article

Review of Signature Recognition Using Deep Learning with Dataset Profiling for Career Pathway Recommendation

Aspalaila Abdullah, Siti Zarina Mohd Muji, Muhammad Sazlan Abdul Kadar, Athirah Batrisya Ramli, Abd Samad Hasan Basari
article en

Abstract

While graphology has historically faced scepticism due to subjectivity, recent Deep Learning (DL) integrations—specifically Convolutional Neural Networks (CNN)—offer a path toward objective personality assessment. However, a significant research gap persists in the systematic mapping of DL-extracted signature features to standardized psychological frameworks for career guidance. This review synthesizes current literature at the intersection of signature-based graphology, the Holland RIASEC model, and CNN architectures. We critique existing feature extraction techniques and classification frameworks, revealing a critical lack of large-scale, standardized datasets and a "black-box" interpretability problem that hinders clinical adoption. By evaluating the synergy between automated trait detection and career typology, this paper identifies three primary research voids: the absence of cross-cultural validation, the need for hybrid models that combine psychological theory with raw pixel data, and the ethical implications of AI-driven recruitment. Ultimately, this review provides a roadmap for transitioning AI-enhanced graphology from a niche experimental tool to an evidence-based framework for personalized career counselling.

International Journal of Sustainable Construction Engineering and Technology (Universiti Tun Hussein Onn Malaysia)
Tun Hussein Onn University of Malaysia (MY)
Universiti Tun Hussein Onn Malaysia
Openalex Percentile: Top 7%
Personality Traits and Psychology
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