Abstract Handwriting Analysis is a method to understand and predict the characteristic traits of a person based on his handwriting style. Graphology is the scientific term used for handwriting analysis. Professional handwriting examiners, called graphologists, manually study and understand the handwriting of an individual to classify the writers personality. Nevertheless, the manual process of handwriting analysis is time-consuming, costly and depends majorly on the skills of the graphologists. To make this process computerized we extracted several features of handwriting samples and classified the writer into 5 personality traits namely Energetic, Extrovert, Introvert, Sloppy and Optimistic. Histogram of oriented gradient(HOG) extracts the features from the handwriting sample of the writer which serves as an input for the Support Vector Machine model to give output as the personality trait of the person. For this paper, digital handwriting sample data of 50 different users were collected. The proposed system predicts the personality trait of a person with 80% correctness using the Polynomial kernel. In this paper, we propose a computerized method for personality trait prediction based on the users handwriting. Two different methods are applied to the same handwriting sample data to measure and compare the performance of the proposed system.