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.
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This work was supported by the National Natural Science Foundation of China under Grant 61703316.
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Guan, J., Zhu, A. (2020). Light Textspotter: An Extreme Light Scene Text Spotter. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_50
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DOI: https://doi.org/10.1007/978-3-030-63820-7_50
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