Exploring Anchor-Free Approach for Reading Chinese Characters

Zhuoyao Wang, Yiqun Wang, Zhao Zhou, Han Yuan, Cheng Jin

Abstract: Scene text spotting has achieved an impressive performance over recent years. Currently, most text localization methods are designed with the text line instance. We argue that building a character-level spotting network is more suited to recognize the Chinese of text and Chinese is also common in scene text images. In this paper, we explore an anchor-free spotting framework that treats a character as a single point. To better capture Chinese character features, we first use the Canny edge detectors and superimpose the obtained edge information onto the RGB image channel. After that, a feed-forward network is set up and the inference can be processed in a single network forward-pass, without complex post-processing steps. Experiments are performed on the Chinese text dataset and the quantitative comparisons demonstrate the effectiveness of the anchor-free approach.