Handwriting – Based Behaviour Pattern Detection Using Convolutional Neural Networks

Abstract

Handwriting is not just a way of writing; it reflects how a person thinks, feels, and behaves. It acts as a brain imprint that shows each person’s unique personality. This research uses Convolutional Neural Networks (CNNs), a type of deep learning, to detect behaviour patterns automatically from handwriting images. This research focuses on analyzing handwriting characteristics to scientifically infer personality traits from writing patterns and structures. The handwriting images were processed through grayscale conversion, noise removal, thresholding, and normalization. For model development, we divided the data into training, validation, and testing sets and used them to train the CNN model. Along with overall classification, selected handwriting samples were studied to analyze behaviour related features such as slant, margin, line spacing, word spacing, size consistency, baseline consistency and pressure. These features help understand personality traits like emotional stability, clarity of thought, confidence, and how a person interacts with others. This work can find practical use in fields such as recruitment, teaching, forensic examinations, counseling, and mental health services, where having a clear understanding of a person’s character and behaviour is highly valuable.

Keywords: Graphology, CNN, Personality traits, Deep Learning, Behaviour Prediction.