Prototype Calibration with Synthesized Samples for Zero-Shot Chinese Character Recognition

Abstract

Zero-shot Chinese character recognition aims to recognize unseen characters that have never appeared in training. Recently, many methods learn a cross-modal alignment between character samples and auxiliary semantic data like glyph templates in training, and directly employ it to recognize unseen characters by retrieving the class with most similar semantics. However, these approaches suffer from the domain shift problem, which means that the learned alignment shows a deviation on unseen characters. To alleviate this problem, we generate unseen character samples to calibrate the shifted prototypes in the feature space. Specifically, we train a cross-modal prototype classifier and a generator conditioned on glyph templates, then use the generator to synthesize unseen character samples to calibrate the prototypes of the classifier. The calibration process does not require any extra training. Experiments on a handwritten dataset and a nature scene dataset show the superiority of our method and the effectiveness of prototype calibration.