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The classification of handwriting into different categories, such as age, gender, and nationality, has several applications. In forensics, handwriting classification helps investigators focus on a certain category of writers. However, only a few studies have been carried out in this field. Classification of handwriting into a demographic category is generally performed in two steps: feature extraction and classification. The performance of a system depends mainly on the feature extraction step because characterizing features makes it possible to distinguish between writers. In this study, we propose several geometric features to characterize handwritings and use these features to perform the classification of handwritings with regards to age, gender, and nationality. Features are combined using random forests and kernel discriminant analysis. Classification rates are reported on the QUWI dataset, reaching 74.05% for gender prediction, 55.76% for age range prediction, and 53.66% for nationality prediction when all writers produce the same handwritten text and 73.59% for gender prediction, 60.62% for age range prediction, and 47.98% for nationality prediction when each writer produces different handwritten text.

Automatic prediction of age, gender, and nationality in offline handwriting