Uncovering Major Age-Related Handwriting Changes by Unsupervised Learning

Understanding how handwriting (HW) style evolves as people get older may be key for assessing the health status of elder people. It can help, for instance, distinguishing HW change due to a normal aging process from change triggered by the early manifestation of a neurodegenerative pathology. We present, in this paper, an approach, based on a 2-layer clustering scheme that allows uncovering the main styles of online HW acquired on a digitized tablet, with a special emphasis on elder HW styles. The 1st level separates HW words into writer-independent clusters according to raw spatial-dynamic HW information, such as slant, curvature, speed, acceleration and jerk. The 2nd level operates at the writer level by converting the set of words of each writer into a Bag of 1st Layer Clusters, that is augmented by a multidimensional description of his/her writing stability across words. This 2nd layer representation is input to another clustering algorithm that generates categories of writer styles along with their age distributions. We have carried out extensive experiments on a large public online HW database, augmented by HW samples acquired at Broca hospital in Paris from people mostly between 60 and 85 years old. Unlike previous works claiming that there is only one pattern of HW change with age, our study reveals basically three major HW styles associated with elder people, among which one is specific to elders while the two others are shared by other age groups