A method for automatic classification of gender based on text- independent handwriting

Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. Out of all the applications gender prediction is mainly admired topic among researchers. The relation between gender and handwriting can be seen from the physical appearance of the handwriting. This research work predicts gender from handwriting using the landmarks of differences between the two genders. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. The experimental results obtained on a dataset of 282 writers with 2 samples per writer shows that the proposed method attains appealing performance on writer detection and text-independent environment.

Automated summary

Handwriting recognition is utilized to predict demographic traits such as age, gender, and nationality, with gender prediction being an especially popular topic among researchers. By analyzing the physical appearance of handwriting, this research successfully predicts gender using differences between male and female handwriting. Through the extraction of features like slantness, area, and perimeter, and the application of classifiers such as SVM and Majority Voting, the proposed method demonstrates appealing performance in writer detection and text-independent environments with experimental results on 282 writers.