A Subtractive Clustering Scheme for Text-Independent Online Writer Identification

This paper proposes a text-independent writer identification framework for online handwritten text. The method utilizes an unsupervised learning scheme termed 'subtractive clustering' to discover the unique writing styles of a given author. Subtractive clustering has been adopted in the literature for the problems of image segmentation and speaker identification. To the best of our knowledge, its applicability in the domain of writer identification is yet to be explored. Unlike traditional clustering techniques such as k-means and fuzzy c-means, the subtractive clustering algorithm does not rely on the initial choice of seed points. Instead, it locates the high density regions in the feature space, and this make this scheme an interesting exploration to capture the writing styles of an author (referred to as 'prototypes'). The discovered prototypes from the clustering algorithm are subsequently employed to score the authorship of an unknown handwritten text. In addition, inspired from the tf-idf approach used in document retrieval, we propose a modified scoring scheme for identifying the writer. The efficacy of the algorithms are evaluated on the paragraphs from the IAM-Online Handwritten Database.