Handwriting Styles Clustering: Feature Selection and Feature Space Analysis based on Online Input

In this paper, we perform a detailed analysis of feature extraction approaches and existing solutions for the problem of handwriting styles clustering. We consider a number of handcrafted features for handwritten text, perform a feature space analysis for finding the best set of extracted features and build an expert system for identifying and clustering handwriting styles using unsupervised methods based on this set. We observed an improvement in clustering results by analyzing the described in this paper metrics for clustering evaluation (such as the Dunn index, silhouette, within- and between-cluster sums of squared errors), therefore we can conclude that the clustering based on such a subset is much more efficient. The results outlined below can be used for handwriting style determination, which is an important step in designing systems for text recognition and localization, authentication and verification tasks, and also can be applied to the problem of the detection of the mental or nervous disorders.