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

Abstract

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.

National characteristics and variation in Arabic handwriting

“…An alternative approach is to use image processing to record features from samples of handwriting and to use computer algorithms to extract features which can then be correlated to people from different countries [24]. Using this, success rates for attributing nationality were around 40-50%, somewhat lower than in this study, although more countries were included.…”

A novel approach for handedness detection from off-line handwriting using fuzzy conceptual reduction

“…This feature makes it possible to distinguish between fast writers who produce smooth handwriting and slow writers who produce "ortuous"/twisted handwriting [36] by finding the longest line in the middle of the character shape. This feature has a PDF vector of 10.…”

“…The following subsections describe the features considered in this study. It is to be noted that these features do not correspond to a single value but are defined by a probability distribution function (PDF) extracted from the handwriting images to characterize the writer's individuality [35,36]. The PDF describes the relative likelihood for a certain feature to take on a given value.…”

“…For each pixel p, we consider the 2 × N + 1 neighboring pixels centered at p. The linear regression of these pixels is calculated to give the tangent at the pixel p [36].…”

“…The PDF of the curvatures is computed as a vector whose size has been empirically set to 100 (s pixels in each side) [36].…”