A Semi-Supervised Learning Approach for Traditional Chinese Scene Text Detection

With the advancement of multimedia technology, the information in surrounding environment has becoming accessible. In particular, automatic scene text detection is essential for subsequent text recognition, understanding and analysis. However, most existing methods are primarily designed for English, while those for other languages are scarce. In this paper we present a traditional Chinese scene text detector, built upon a robust object detector trained with labeled and unlabeled data via semi-supervised learning. Moreover, we expand the limited labeled data by data synthesis and a data augmentation method. We demonstrate the effectiveness of the proposed method through extensive experiments, and examine the design choices in developing a practical system that can instantly and accurately detect traditional Chinese texts in complex scenes.