PS-LSTM: Capturing Essential Sequential Online Information with Path Signature and LSTM for Writer Identification
Writer identification plays a significant role in several applications including forensic trace evidence identification, mobile bank transaction authentication, and handwritten character/text recognition. However, performing writer identification requires a considerable amount of experimental work and labor, as well as professional skills. In this paper, we propose a novel path-signature long short-term memory (PS-LSTM) recurrent neural network for writer identification that contributes as follows: 1) A mathematical feature set, path signature, is successfully applied to writer identification to characterize the essential geometric and analytic properties of pen-tip trajectory, which help distinguish between the diverse writing styles of different people, especially in confusing situations. 2) Different iterated integrals of path signature are investigated to model the local curvature nature of the pen-tip trajectory for writer identification. 3) Our study is the first to demonstrate the significance of integrating path signature with long short-term memory for writer identification. On Database CASIA-OLHWDN1.0, the proposed PS-LSTM writer identification achieves significantly superior results and outperforms previous works.