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STAN: A sequential transformation attention-based network for scene text recognition
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DOI：10.1016/j.patcog.2020.107692.png)
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摘要
En 中文
Scene text with an irregular layout is difficult to recognize. To this end, a Sequential Transformation Attention-based Network (STAN), which comprises a sequential transformation network and an attention-based recognition network, is proposed for general scene text recognition. The sequential transformation network rectifies irregular text by decomposing the task into a series of patch-wise basic transformations, followed by a grid projection submodule to smooth the junction between neighboring patches. The entire rectification process is able to be trained in an end-to-end weakly supervised manner, requiring only images and their corresponding groundtruth text. Based on the rectified images, an attention-based recognition network is employed to predict a character sequence. Experiments on several benchmarks demonstrate the state-of-the-art performance of STAN on both regular and irregular text. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Scene text recognition
Scene text rectification
Optical character recognition
Deep learning
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
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