基于复小波的脱线手写体笔迹鉴别

进行脱线笔迹鉴别时,笔迹特征只能从手写体图像中提取,且无法获取书写时的动态信息,导致了脱线笔迹鉴别的正确率不是很高。为了进一步提高脱线手写体笔迹鉴别的正确率,提出基于复小波的GGD模型方法对笔迹进行鉴别。与传统小波GGD模型方法比较,复小波GGD模型方法具有时移不变性和良好的方向分析能力,在提取纹理特征方面更有效。实验结果表明,该方法在鉴别正确率上有很大的提升。

Handwriting-based writer identification with complex wavelet transform

A challenging problem of off-line text-independent writer identification is that plenty of dynamic writing information with the handwriting images can not be extracted as writing features, this results in high error rate in off-line writer identification. In order to enhance the performance of off-line writer identification, a complex wavelet-based Generalized Gaussian Distribution (GGD) method was proposed. Compared with the traditional wavelet-based GGD method, the novel method is more efficient on texture extraction due to its time-invariant features and good directional analysis. Experimental results show that the proposed method achieves a better performance of writer identification.