Online writer identification using statistical modeling-based feature embedding

Abstract

Writer identification is the task of specifying the genuine writer according to their handwriting across a set of enrolled subjects which is a noteworthy research topic in the community of document analysis and recognition. In this paper, a novel framework based totally on identity vector is introduced for the online writer identification task. In the proposed framework, the sequence of extracted feature vectors from each handwriting sample is embedded into a fixed-length vector, referred to as identity vector (i-vector), to capture the long-term sequence-level writer-related characteristics, and then passed to the next stage for classification. Several techniques for feature normalization and intra-class variation reduction techniques in the i-vector domain such as within-class covariance normalization and regularized linear discriminant analysis are also investigated. We extensively evaluate the introduced framework on the popular database, CAISA, for English and Chinese language in various scenarios, such as multi-language and cross-language. Experimental results show, in the best cases, the proposed framework could achieve 98.68% accuracy on English dataset and 96.03% on Chinese dataset of the CAISA database. These obtained results indicate an improvement over the best reported result of the current state-of-the-art approaches with the exception of fully end-to-end approaches which have their own serious limitation in the real applications. In addition to the accuracy improvement, due to its low computational load it has the potential to be implemented on the handheld digital devices.