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Published in:
2020 | OriginalPaper | Chapter
Light Textspotter: An Extreme Light Scene Text Spotter
Authors : Jiazhi Guan, Anna Zhu
Published in: Neural Information Processing
Publisher: Springer International Publishing
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Abstract
Scene text spotting is a challenging open problem in computer vision community. Many insightful methods have been proposed, but most of them did not consider the enormous computational burden for better performance. In this work, an extreme light scene text spotter is proposed with a teacher-student (TS) structure. Specifically, light convolutional neural network (CNN) architecture, Shuffle Unit, is adopted with feature pyramid network (FPN) for feature extraction. Knowledge distillation and attention transfer are designed in the TS framework to boost text detection accuracy. Cascaded with a full convolution network (FCN) recognizer, our proposed method can be trained end-to-end. Because the resource consumption is halved, our method runs faster. The experimental results demonstrate that our method is more efficient and can achieve state-of-the-art detection performance comparing with other methods on benchmark datasets.