k-NN classification of handwritten characters via accelerated GAT correlation
k-NN classification of handwritten characters via accelerated GAT correlation
复制标题
通过加速 GAT 相关性对手写字符进行 k-NN 分类
DOI：
10.1016/j.patcog.2013.05.005
复制
发表时间：
2014
影响因子：
8
通讯作者：
Yukihiko Yamashita
中科院分区：
文献类型：
--
作者：
Toru Wakahara;Yukihiko Yamashita
本文研究了在有限的数据量下，通过变形容忍模板匹配技术增强k-近邻手写体字符分类能力的问题。我们比较了三种匹配技术：传统的简单相关，切线距离，和全球仿射变换（GAT）相关。虽然k-NN分类方法简单而强大，但它消耗了大量的时间。因此，为了降低匹配墨水NN分类的计算成本，我们提出通过重新制定其计算模型和采用高效的查找表来加速GAT相关方法。在IPTP CDROM 1B手写体数字库上进行的识别实验表明，简单相关、切线距离和加速GAT相关的匹配技术分别取得了97.07%、97.50%和98.70%的识别率。切线距离和加速GAT关联与简单关联的计算时间比分别为26.3和36.5：1.0。
This paper addresses the problem of reinforcing the ability of thek-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although thek-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching ink-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.