AGE and GENDER PREDICTON from HAND WRITTING TEXT using ATTENTION-BASED CNN

Abstract

The goal is to predict the age range and gender of a person based on their handwriting. Two feature extraction methods, Histogram of Oriented Gradients and Linear Discriminant Analysis, are employed, along with two machine learning techniques, Support Vector Machine and Attention Based Convolutional Neural Network. For each feature extraction method, the system predicts the age range and gender using SVM and ABCNN. The system provides accuracy metrics for each prediction to decide which technique is best, indicating the reliability of the age and gender estimations. This is designed to work in real-time, enabling quick predictions for incoming handwriting samples. Key Words : Histogram of Oriented Gradients (HOG), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Attention Based Convolutional Neural Network (ABCNN) , Convolutional Neural Network (CNN)