Analyzing Handwriting to Infer Personality Traits

Graphology, the study of handwriting to infer personality traits,

served as the foundation for my recent project, which aimed to

bring traditional handwriting analysis into the realm of modern artificial intelligence. In this project, we developed a deep learning

model specifically designed to classify personality traits from handwriting samples. Given the limited dataset available, this task presented a unique set of challenges. The model initially utilized a convolutional neural network (CNN) architecture, leveraging advanced

variations such as VGG, ResNet, Inception, and DenseNet to analyze and extract intricate features from the handwriting images.

These features were then used to classify the samples into different personality categories. Through rigorous training and validation

processes, including data augmentation techniques to enhance the

dataset, the model achieved a promising accuracy of 70%. This result is particularly notable considering the novelty of the approach

and the constrained data. The success of this project underscores

the potential of deep learning to identify patterns in handwriting

that correlate with personality traits, providing a modern AI-based

augmentation to traditional graphological methods.