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An Attention-Based Recognizer for Scene Text

Yugang Li1, *, Haibo Sun1

1 Academy of Broadcasting Science, Beijing, 100866, China.

* Corresponding Author: Yugang Li. Email: email.

Journal on Artificial Intelligence 2020, 2(2), 103-112. https://doi.org/10.32604/jai.2020.010203

Abstract

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. Although STR method has been greatly developed, the existing methods still can't recognize any shape of text, such as very rich curve text or rotating text in daily life, irregular scene text has complex layout in two-dimensional space, which is used to recognize scene text in the past Recently, some recognizers correct irregular text to regular text image with approximate 1D layout, or convert 2D image feature mapping to one-dimensional feature sequence. Although these methods have achieved good performance, their robustness and accuracy are limited due to the loss of spatial information in the process of two-dimensional to one-dimensional transformation. In this paper, we proposes a framework to directly convert the irregular text of two-dimensional layout into character sequence by using the relationship attention module to capture the correlation of feature mapping Through a large number of experiments on multiple common benchmarks, our method can effectively identify regular and irregular scene text, and is superior to the previous methods in accuracy.

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Cite This Article

Y. Li and H. Sun, "An attention-based recognizer for scene text," Journal on Artificial Intelligence, vol. 2, no.2, pp. 103–112, 2020.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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