Forecasting Personality Based On Calligraphy Using CNN and MLP - N, Nijil Raj; Thaha, Mohammed; Shaji, Sushlin Grace; S, Shibina (International Journal of Computer Sciences and Engineering, 2020)
The way of living has modified since the digital age. Everything can be dealt with a tip of the finger, but all these luxuries are at risk at a cost of protection or fraud. Handwritten script or calligraphy explore about a person's personality. It tells concerning the character of the person and predicts the attribute like optimistic, Social Maturity, balanced, shy within the calligraphy as writing is linked with brain and it subconsciously leaves a sample. Various forms of calligraphy styles taken into thought are slope, baseline, top margin, word size, line spacing, word spacing, left or right or normal slant or irregular of the sentence, etc. The complete system evaluates the script based on the above-mentioned calligraphy styles and it is divided into three modules with the primary module being input the image of written text, then apply the preprocess to removes noise and sharpens the contrast of the image for better results. Extract the 7 features from each image in the dataset, then apply Convolutional Neural Network (CNN) combined with Multi Layer Perceptron(MLP). The proposed system reveals a better result compared to literature survey.