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...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 suboptimal detection perfor⁃mance.The experimental results on three benchmarks demonstrate that the proposed method can consistently improve the performance with⁃out sacrificing inference speed.展开更多
基金supported by ZTE Industry⁃University⁃Institute Coopera⁃tion Funds under Grant No.HC⁃CN⁃20200717012.
文摘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 suboptimal detection perfor⁃mance.The experimental results on three benchmarks demonstrate that the proposed method can consistently improve the performance with⁃out sacrificing inference speed.
