Abstract:
This paper presents a study on assessing the effectiveness
of machine learned features to predict gender of
writers from images of handwriting. Pre-trained Convolutional
Neural Networks have been employed as feature extractors to
discriminate male and female handwriting while classification is
carried out using a number of classifiers, Linear Discriminant
Analysis (LDA) being the most effective. Feature extraction is
carried out by changing the scale of observation using word,
patch and page images. Experiments are carried out on English
and Arabic handwriting samples of the QUWI database and the
realized results demonstrate the effectiveness of machine learned
features in predicting gender from handwriting.
