Zero-shot Handwritten Chinese Character Recognition with hierarchical decomposition embedding

Abstract Handwritten Chinese Character Recognition (HCCR) is a challenging topic in the field of pattern recognition due to large-scale character vocabulary, complex hierarchical structure, various writing styles, and scarce training samples. In this paper, we explored the hierarchical knowledge of Chinese characters and presented a novel zero-shot HCCR method. First, we handled the relations between the characters and their primitives, such as radicals and structures, to obtain a tree layout of primitives. Then, we presented a novel zero-shot hierarchical decomposition embedding method to encode the tree layout into a semantic vector. Next, we devised a Convolutional Neural Network (CNN) based framework to learn both radicals and structures of characters via the semantic vector. As different Chinese characters share some common radicals and structures, our method is able to recognize new categories without any labeled samples from them. Moreover, our method is effective in both traditional HCCR and zero-shot HCCR tasks. It achieves competitive performance on the traditional experiment setting and significantly surpasses the state-of-the-art methods on the zero-shot experiment setting.