Multi-oriented scene text detection by fixed-width multi-ratio rotation anchors

Multi-oriented scene text detection by fixed-width multi-ratio rotation anchors
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通过固定宽度多比例旋转锚点进行多方向场景文本检测

DOI:
10.1016/j.compeleceng.2021.107428
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发表时间:
2021-10
影响因子:
4.3
通讯作者:
Lingzi Jiang
Lingzi Jiang
中科院分区:
计算机科学3区
文献类型:
--
作者:
Beiji Zou;Wenjun Yang;Shu Liu;Lingzi Jiang

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场景文本检测在许多实际应用中起着重要的作用。本文提出了一个面向多个场景的文本检测框架,它包括三个主要模块。我们利用框架前面的深度残余网络来学习文本表示。引入了一组固定宽度、多比率旋转锚点来滑过卷积特征图,并生成具有方向信息的文本提案。然后,网络内的递归架构被无缝连接,其中提案的顺序上下文被编码,以促进文本行的构建。在两个ICDAR基准上进行了大量的实验,以证明我们的方法的有效性。
Scene text detection plays an important role in many real-world applications. In this paper, we propose a multi-oriented scene text detection framework, which includes three main modules. We utilize a deep residual network in the front of the framework to learn text representations. A set of fixed-width, multi-ratio rotation anchors is introduced to slide over convolutional feature maps and generate the text proposals with orientation information. An in-network recurrent architecture is then seamlessly connected, where the sequential context of proposals is encoded in order to facilitate the construction of text lines. Extensive experiments are conducted on two ICDAR benchmarks to demonstrate the effectiveness of our approach.
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