Label Enhancement for Scene Text Detection

Abstract: Segmentation-based scene text detection has drawn a great deal of attention, as it can describe the text instance with arbitrary shapes based on its pixel-level prediction. However, most segmentation-based methods suffer from complex post-processing to separate the text instances which are close to each other, resulting in considerable time consumption during the inference procedure. A label enhancement method is proposed to construct two kinds of training labels for segmentation-based scene text detection in this paper. The label distribution learning (LDL) method is used to overcome the problem brought by pure shrunk text labels that might result in sub-optimal detection performance. The experimental results on three benchmarks demonstrate that the proposed method can consistently improve the performance without sacrificing inference speed.

Keywords: label enhancement; scene text detection; semantic segmentation