Analyzing Handwriting to Infer Personality Traits: A Deep Learning Framework
Ahmed Mohamed Ahmed Sayed, Ammar Wael Gaber Selim, Abdelaziz Ashraf, Ehab Emam · 2024
Personality prediction through handwriting analysis represents an exciting intersection of graphology and advanced machine learning technologies, aiming to derive insights into individual personality traits from handwritten samples. This research employs sophisticated deep learning models, including Convolutional Neural Networks (CNNs), VGG16, DenseNet201, ResNet, and InceptionV3, utilizing the IAM Handwriting Database and a proprietary dataset titled “Personality Prediction Using Handwriting Images.” These resources provide a diverse basis for our models to extract and learn from the unique behavioral patterns evident in handwriting nuances like letter size, slant, and pressure. Our methodology incorporates extensive preprocessing and data augmentation techniques to optimize the data for effective model training. The VGG16 model showcased a remarkable performance, with accuracy of 73.8%, underscoring the potential of deep learning in psychological profiling. This project not only adds a technological dimension to personality assessment but also highlights the ongoing need for architectural refinements to achieve an ideal balance between computational efficiency and predictive accuracy. By enhancing our methodologies and expanding our analytical tools, future research could further increase the robustness and reliability of personality predictions, broadening the application of this technology in psychological assessment and behavioral science.