Graphology based handwritten character analysis for human behaviour identification

• Main Authors: Subhankar Ghosh, Palaiahnakote Shivakumara, Prasun Roy, Umapada Pal, Tong Lu
• Format: Article
• Language: English
• Published: Wiley 2020-01-01
• Series: CAAI Transactions on Intelligence Technology
• Subjects: handwritten character recognition; feature extraction; behavioural sciences computing; human behaviour identification; graphology based handwriting analysis; human intervention; behavioural analysis; handwritten english; person behaviours; structural features; cursive lines; straight lines; stroke thickness; contour shapes; aspect ratio; geometrical properties; isolated character images; automatic privacy projected system; graphological rules
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0051

Graphology-based handwriting analysis to identify human behavior, irrespective of applications, is interesting. Unlike existing methods that use characters, words and sentences for behavioural analysis with human intervention, we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours. The proposed method extracts structural features, such as loops, slants, cursive, straight lines, stroke thickness, contour shapes, aspect ratio and other geometrical properties, from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules. The derived hypothesis has the ability to categorise the personal, positive, and negative social aspects of an individual. To evaluate the proposed method, an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups. This automatic privacy projected system is available on the website (http://subha.pythonanywhere.com). For quantitative evaluation of the proposed method, several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options. The automatic system receives 5300 responses from the users, for which, the proposed method achieves 86.70% accuracy.