Stroke Bank: A High-Level Representation for Scene Character Recognition
Stroke Bank: A High-Level Representation for Scene Character Recognition
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DOI：
10.1109/icpr.2014.501
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发表时间：
2014-08
期刊：
影响因子：
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通讯作者：
Song Gao;Chunheng Wang;Baihua Xiao;Cunzhao Shi;Zhong Zhang
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文献类型：
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作者：
Song Gao;Chunheng Wang;Baihua Xiao;Cunzhao Shi;Zhong Zhang
场景图像中包含的文本信息对图像理解非常有用。在本文中，我们提出了一个高层次的表示命名为笔划银行的场景字符识别。受对象库工作的启发，我们训练笔划检测器，并使用检测器的最大输出作为特征。具体来说，我们根据标记的关键点收集中风检测器的训练样本。我们还建议将每个笔画检测器的分类区域限制在特定的局部区域，这减轻了计算负担，同时保留了辨别力。在基准数据集上的实验证明了该方法的有效性，其结果优于现有算法。
Text information contained in scene images is very useful for image understanding. In this paper, we propose a high-level representation named stroke bank for scene character recognition. Inspired by the work of object bank, we train stroke detectors and use detectors' maximal output as features. Specifically, we collect training samples for stroke detectors based on labeled key points. We also propose to restrict classification areas of each stroke detector to particular local regions, which alleviates computation burden and retains discrimination power at the same time. Experiments on benchmark datasets demonstrate the effectiveness of our method and the results outperform state-of-the-art algorithms.