An improved handwritten Chinese character recognition system using support vector machine
An improved handwritten Chinese character recognition system using support vector machine
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DOI：
10.1016/j.patrec.2005.03.006
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发表时间：
2005-09-01
影响因子：
5.1
通讯作者：
Suen, CY
中科院分区：
文献类型：
--
作者：
Dong, JX;Krzyzak, A;Suen, CY
本文介绍了改进一个汉字识别系统的几种技术。在具有数千类的大数据集上增强非线性归一化、特征提取和调整支持向量机的核参数，有助于提高系统的整体性能。增强的非线性归一化方法不仅解决了原始Yamada等人中的混叠问题。的非线性归一化方法，而且还避免了归一化图像的外围区域中的不适当的笔画失真。支持向量机是第一次在一个由数百万个样本和数千个类组成的大型数据集上进行测试。该识别系统在手写体汉字库ETL9B上取得了99.0%的识别率。(c)2005 Elsevier B.V.保留所有权利。
This paper describes several techniques improving a Chinese character recognition system. Enhanced nonlinear normalization, feature extraction and tuning kernel parameters of support vector machine on a large data set with thousands of classes, contribute to improvement of the overall system performance. The enhanced nonlinear normalization method not only solves the aliasing problem in the original Yamada et al.'s nonlinear normalization method but also avoids the undue stroke distortion in the peripheral region of the normalized image. The support vector machine is for the first time tested on a large data set composed of several million samples and thousands of classes. The recognition system has achieved a high recognition rate of 99.0% on ETL9B, a handwritten Chinese character database. (c) 2005 Elsevier B.V. All rights reserved.