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基于多尺度图匹配核的场景单字识别方法

史存召 王春恒 肖柏华 张阳 高嵩

史存召, 王春恒, 肖柏华, 张阳, 高嵩. 基于多尺度图匹配核的场景单字识别方法. 自动化学报, 2014, 40(4): 751-756. doi: 10.3724/SP.J.1004.2014.00751
引用本文: 史存召, 王春恒, 肖柏华, 张阳, 高嵩. 基于多尺度图匹配核的场景单字识别方法. 自动化学报, 2014, 40(4): 751-756. doi: 10.3724/SP.J.1004.2014.00751
SHI Cun-Zhao, WANG Chun-Heng, XIAO Bai-Hua, ZHANG Yang, GAO Song. Multi-scale Graph-matching Based Kernel for Character Recognition from Natural Scenes. ACTA AUTOMATICA SINICA, 2014, 40(4): 751-756. doi: 10.3724/SP.J.1004.2014.00751
Citation: SHI Cun-Zhao, WANG Chun-Heng, XIAO Bai-Hua, ZHANG Yang, GAO Song. Multi-scale Graph-matching Based Kernel for Character Recognition from Natural Scenes. ACTA AUTOMATICA SINICA, 2014, 40(4): 751-756. doi: 10.3724/SP.J.1004.2014.00751

基于多尺度图匹配核的场景单字识别方法

doi: 10.3724/SP.J.1004.2014.00751

Multi-scale Graph-matching Based Kernel for Character Recognition from Natural Scenes

Funds: 

Supported by National Natural Science Foundation of China (60933010, 61172103, 61271429)

  • 摘要: 由于自然场景中的文字具有较大的类内间距, 因此识别场景文字具有很大的挑战性. 本文提出了一种基于多尺度图匹配核的场景单字识别方法. 为了利用字符特有的结构特征, 将每幅图像表示为基于不同网格划分的无向图, 通过计算两个无向图之间图匹配的最优能量值来得到两幅图像的相似度, 由于图匹配在计算每个节点的最佳匹配节点时也考虑了相邻节点之间的空间位置约束, 因此可以应对具有一定形变的文字. 通过图匹配得到的两幅图像之间的相似度很适合用来构造支持向量机的核矩阵. 本文将不同尺度网格划分下得到的核矩阵进行多核融合, 使得最终得到的核矩阵更加地鲁棒. 在国际公开场景文字识别数据集Chars74k和ICDAR03-CH上的实验结果表明, 本方法取得了高于国际上已发表的其他方法的单字识别率.
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出版历程
  • 收稿日期:  2012-05-22
  • 修回日期:  2013-09-27
  • 刊出日期:  2014-04-20

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