Writer identification in Indic scripts: a stroke distribution based approach

This paper proposes to represent an offline handwritten document with a distribution of strokes over an alphabet of strokes for writer identification. A data driven approach for stroke alphabet creation is done as follows: strokes are extracted from the image, using a regression method, extracted strokes are represented as fixed length vectors in a vector space, strokes are clustered into stroke categories to create a stroke alphabet. The paper proposes a clustering method with a new clustering score whereby an optimal number of clusters (categories) are automatically identified. For a given document, based on the frequency of occurrence of elements in the stroke alphabet, a histogram is created that represents the writer's writing style. Support Vector Machine is used for the classification purpose. Offline handwritten documents written in two different Indic languages, viz., Telugu and Kannada, were considered for the experimentation. Results comparable to other methods in the literature are obtained from the proposed method.