基于融合特征和LS-SVM的脱机手写体汉字识别

提出的脱机手写体汉字识别系统主要研究特征提取和分类识别两个模块.特征提取模块主要包括采用基于不变矩和弹性网格技术的串行特征融合方法,所得到的特征向量不仅充分反映了手写体汉字的全局和局部特征,而且具有很强的区分表达能力.分类识别模块将神经网络多类分类策略与最小二乘支持向量机相结合,所得到的分类器不仅识别率高、泛化能力强,而且有效地解决了多类分类问题.实验证明本文提出的识别系统能够取得很好的识别效果.

Off-line handwritten Chinese character recognition based on fusion features and LS-SVM

The proposed off-line handwritten Chinese character recognition system was composed of a feature extraction module and a recognition module. In the feature extraction module, the orthogonal Zernike moments and the elastic mesh technique were combined to get fusion features, which present the global and local features of handwritten Chinese characters and have great discriminative capability. As for the classification module, one approach that is very similar to the neural network classification strategy was used with the Least Square Vector Machine (LS-SVM), which not only has the excellent performance of generalization and recognition accuracy, but also can solve the multi-classification issue effectively. Experimental results indicated that the proposed method could get good recognition results.