Chinese Character Recognition with Augmented Character Profile Matching

Chinese character recognition (CCR) has drawn continuous research interest due to its wide applications. After decades of study, there still exist several challenges, e.g., different characters with similar appearance and the one-to-many problem. There is no unified solution to the above challenges as previous methods tend to address these problems separately. In this paper, we propose a Chinese character recognition method named Augmented Character Profile Matching (ACPM), which utilizes a collection of character knowledge from three decomposition levels to recognize Chinese characters. Specifically, the feature maps of each character image are utilized as the character-level knowledge. In addition, we introduce a radical-stroke counting module (RSC) to help produce augmented character profiles, including the number of radicals, the number of strokes, and the total length of strokes, which characterize the character more comprehensively. The feature maps of the character image and the outputs of the RSC module are collected to constitute a character profile for selecting the closest candidate character through joint matching. The experimental results show that the proposed method outperforms the state-of-the-art methods on both the ICDAR 2013 and CTW datasets by 0.35% and 2.23%, respectively. Moreover, it also clearly outperforms the compared methods in the zero-shot settings. Code is available at https://github.com/FudanVI/FudanOCR/character-profilematching.