Improving Text-Independent Chinese Writer Identification with the Aid of Character Pairs

Abstract

Text-independent Chinese writer identification does not depend on the text content of the query and reference handwritings. In order to deal with the uncertainty of the text content, text-independent approaches usually give special attention to the global writing style of handwriting, rather than the properties of each individual character or word. Thanks to the existence of high-frequency characters, some characters probably appear in both the query and reference handwritings in most cases. If character images in the query handwriting are similar to those in the reference handwriting, this query handwriting and the corresponding reference handwriting are very likely to be written by the identical writer. In this paper, we exploit the above characteristic to improve the performance of Chinese writer identification. We first present an identification scheme using edge co-occurrence feature (ECF). Then, we detect the character pairs in the query and reference handwritings using a two-step framework and propose the displacement field-based similarity (DFS) to determine whether a character pair is written by the identical writer. The character pairs help to re-rank the candidate list obtained by text-independent ECF-based similarity and finally decide the writer of the query handwriting. The proposed method is evaluated on the HIT-MW and CASIA-2.1 datasets. Experimental results demonstrate that our proposed method outperforms the existing ones, and its Top-1 accuracy on the two datasets reaches 97.1% and 98.3%, respectively.