Handwriting Analysis and Personality Profiling using Image Processing and Machine Learning
Shivani Sopariwala, Dipali Kasat · 2024
Scientific graphology is based on the theory that a person’s psyche, peculiarity, and strophes are expressed through handwriting. On top of developing a broad character sketch, hand writing analysis reveals a person’s health tendencies, ethical dispositions, past events, undetected skills, and possible psychological disorders. This is nothing like a polygraph or oscilloscope read-out, but instead is a direct portrayal of one’s whole self. The proposed system consists of an app that captures image sample of a user’s handwriting for its automated analyses. Analysis of basic attributes like pressure, length, slope, t-bars and set up are used to determine information such as letter spacing, word spacing, and line breaks amongst other things. Next, each image is processed and broken down into images of individual letters. These images are subsequently fed into a machine learning module for a more advanced analysis of personality traits. We concentrate on measuring the following character dimensions; openness to experience, consciousness, amicability, and neuroticism. After that the most common features get emphasized highlighting some personality aspects about the user being studied. It allows the end user to understand themselves based on the most common characteristics which is an important element of self-awareness. Further, this system can also add onto its usefulness by recommending probable career pathways according to these generated individual characteristics to help people decide which way to go in regards to their lifelong development and growth. The app can also be modified to provide suggestions on how to improve your handwriting, offering an all-round better individual experience.