中国科学院自动化研究所机构知识库

Most existing text spotting methods either focus on horizontal/oriented texts or perform arbitrary shaped text spotting with character-level annotations. In this paper, we propose a novel scene text spotting framework to detect and recognize text of arbitrary shapes in an end-to-end manner, using only word/line-level annotations for training. Motivated from the name of TextSnake, which is only a detection model, we call the proposed text spotting framework TextDragon. In TextDragon, a text detector is designed to describe the shape of text with a series of quadrangles, which can handle text of arbitrary shapes. To extract arbitrary text regions from feature maps, we propose a new differentiable operator named RoISlide, which is the key to connect arbitrary shaped text detection and recognition. Based on the extracted features through RoISlide, the CNN and CTC based text recognizer is introduced to make the framework free from labeling the location of characters. The proposed method achieves the state-of-the-art performance on two curved text benchmarks including CTW1500 and Total-Text, and competitive results on the ICDAR 2015 Dataset.