为了更有效地提取手写汉字的特征,提高识别精度,本文提出了一种利用非线性归一化过程产生的坐标变换信息来提取手写汉字有效特征的方法。该方法通过非线性归一化获得各有效像素点在原汉字图像及规整后汉字图像中的坐标变换关系,在原图像上抽取各点特征,在归一化图像上进行网格的均匀划分和特征统计并形成用于分类的特征向量。该方法有效克服了以往先进行归一化预处理方法和动态网格方法的一些不足,兼顾了与传统结构特征提取方法的有效结合。针对HCL2000脱机手写汉字库大字符集样本的实验结果表明,该特征提取方法可有效提高识别精度和特征抽取速度。
Abstract
A new feature extraction method contributing to improvement of the performance of a handwritten Chinese character recognition system is presented. By using enhanced weighted dynamic meshes based on nonlinear normalization, this method not only avoids the zigzags and other undesirable side effects introduced in the original Yamada et al.’s nonlinear normalization method but also avoids additional feature normalization process in the original Lian-Wen Jin et al.’s and WU Tian-lei et al.’s dynamic mesh method. Experiment on HCL2000 shows that our method achieves superior performance.
关键词
人工智能 /
模式识别 /
手写汉字识别 /
非线性归一化 /
加权动态网格 /
特征提取
{{custom_keyword}} /
Key words
artificial intelligence /
pattern recognition /
hand written Chinese character recognition /
nonlinear normalization /
weighted dynamic mesh /
feature extraction
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Kimura F, Wakabayashi T, Tsuruoka S, et al. Improvement of handwritten Japanese character recognition using weighted direction code histogram[J]. Pattern Recognition, 1997, 30(8): 1329-37.
[2] Kato N, Suzuki M, Omachi S, et al. A handwritten character recognition system using directional element feature and asymmetric Mahalanobis distance[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1999, 21(3): 258-262.
[3] Tsukumo J, Tanaka H. Classification of Handprinted Chinese Characters Using Nonlinear Normalization and Correlation Methods[A]. In: Proc. 9th ICPR[C]. Rome, Italy, 1988: 168-171.
[4] Yamada H, Yamamoto K, Saito T. A Nonlinear Normalization Method for Handprinted Kanji Character Recognition—line Density Equalization[J]. Pattern Recognition, 1990, 23(9): 1023-1029.
[5] Lee S W, Park J S. Nonlinear Shape Normalization Methods for the Recognition of Large-set Handwritten Characters[J]. Pattern Recognition, 1994, 27(7): 895-902.
[6] 金连文,徐秉铮. 手写体汉字识别中的一种新的特征提取方法－弹性网格方向分解特征[J].电路与系统学报, 1997, 32(2): 7-12.
[7] 吴天雷, 马少平. 基于重叠动态网格和模糊隶属度的手写汉字特征抽取[J].电子学报,2004, 32(2): 186-190.
[8] 郭军, 蔺志青, 张洪刚.一个新的脱机手写汉字数据库模型及其应用[J]. 电子学报, 2000,(5): 115-116.
[9] 蔺志青,郭军. 一种相似汉字的识别算法[J].中文信息学报 , 2002, 16(5): 44-48
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
教育部跨世纪人才基金和教育部重点科研项目资助(02029)
{{custom_fund}}
PDF(363 KB)
