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Most existing online writer-identification systems require that the textcontent is supplied in advance and rely on separately designed features andclassifiers. The identifications are based on lines of text, entire paragraphs,or entire documents; however, these materials are not always available. In thispaper, we introduce a path-signature feature to an end-to-end text-independentwriter-identification system with a deep convolutional neural network (DCNN).Because deep models require a considerable amount of data to achieve goodperformance, we propose a data-augmentation method named DropStroke to enrichpersonal handwriting. Experiments were conducted on online handwritten Chinesecharacters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classesfrom 420 writers. For each writer, we only used 200 samples for training andthe remaining 3,666. The results reveal that the path-signature feature isuseful for writer identification, and the proposed DropStroke techniqueenhances the generalization and significantly improves performance.

Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN