Personality Prediction based on Handwriting using Machine Learning
Nikita Lemos, Krish Shah, Rajas Rade, Dharmil Shah · 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) · 2018
The lifestyle of humans has modified since digital age where everything may be handled with a tip of the finger, however all those luxuries might return at a value of security or fraud where masking one's identity with a faux one is possible that on the opposite hand isn't possible during a case with handwriting. Handwriting is exclusive to each person like a fingerprint is exclusive to each person. Someone can imitate another person's handwriting for less than a few words creating it distinctive. Handwriting tells about the character of the person as writing is coupled with brain and it subconsciously leaves a path concerning the temperament attribute like optimistic, pessimistic, balanced, shy, etc., which might be detected. Various forms of handwriting styles are taken into thought like slope or angle of the sentence, number of words in a region, left or right slant of the sentence, etc. The complete system evaluates the handwriting samples based on the above-mentioned handwriting styles and it is divided into four modules with the primary module being the input where the image of handwritten text is taken from the user that is followed by image pre-processing that removes noise and sharpens the contrast of the image for better results, that is then passed to the Convolutional Neural Network (CNN) that analyses the input image with the CNN model which is created by performing CNN on the training dataset and labels the input image accordingly and the last module is the output where the labeled images from the previous module is used to find out the percentage of various traits present in the handwriting sample of the subject.