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基于融合特征和LS-SVM的脫機手寫體漢字識別

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

  • 摘要: 提出的脫機手寫體漢字識別系統主要研究特征提取和分類識別兩個模塊.特征提取模塊主要包括采用基于不變矩和彈性網格技術的串行特征融合方法,所得到的特征向量不僅充分反映了手寫體漢字的全局和局部特征,而且具有很強的區分表達能力.分類識別模塊將神經網絡多類分類策略與最小二乘支持向量機相結合,所得到的分類器不僅識別率高、泛化能力強,而且有效地解決了多類分類問題.實驗證明本文提出的識別系統能夠取得很好的識別效果.

     

    Abstract: 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 (LSSVM), 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.

     

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