Bag of features approach for offline text-independent Chinese writer identification

This paper studies offline text-independent writer identification of Chinese handwriting. The Bag of Features method is adopted for Chinese writer identification and performs much better than previous state-of-the-art methods. The feature adopted is scale invariant transform feature (SIFT) descriptor for it can extract local directional information from Chinese characters. Instead of Hard Voting, we use two newly devised coding strategies: Improved Fisher Kernels and Locality-constrained Linear Coding, to encode each SIFT descriptor. To make these coding strategies suitable to this new application area, absolute average pooling function is utilized. At last the K-nearest-neighbor classifier is used to identify the author of a handwriting image. Experimental results are conducted on a newly collected dataset of Chinese handwriting, CASIA Offline DB 2.1. Experimental results show our approach not only outperforms previous state-of-the-art methods, but also the traditional Bag of Word method using Hard Voting.