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• W3003417190 abstract "Scene text recognition has attracted increasing attention in computer vision due to its various applications. Most of the existing scene text recognition methods are under the encoder-decoder framework. In order to improve text feature learning of these methods, Generative Adversarial Networks (GANs) are recently integrated to generate clean text images without distorted letters. However, the existing GANs assume the input images are spatially aligned, while the words in natural images are often in irregular shapes. The misalignment brings a big problem for both image generation and text recognition. In this paper, we present a novel text feature alignment network to solve this problem. Our method can handle both horizontal and vertical images with irregular texts. Our proposed framework is end-to-end trainable, and extensive experiments on several public benchmarks demonstrate its superiority in terms of both effectiveness and efficiency." @default.
• W3003417190 created "2020-02-07" @default.
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• W3003417190 date "2019-11-01" @default.
• W3003417190 modified "2023-09-23" @default.
• W3003417190 title "Scene Text Recognition with Auto-Aligned Feature Generator" @default.
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• W3003417190 doi "https://doi.org/10.1109/icdm.2019.00185" @default.
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