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A Learning Pseudo Bayes Discriminant Method Based on Difference Distribution of Feature Vectors

机译:基于特征向量差分分布的学习伪贝叶斯判别方法

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We developed a learning pseudo Bayes discriminant method, that dynamically adapts a pseudo Bayes discriminant function to a font and image degradation condition present in a text. In this method, the characteristics of character pattern deformations are expressed as a statistic of a difference distribution, and information represented by the difference distribution is integrated into the pseudo Bayes discriminant function. The formation of integrating the difference distribution into the pseudo Bayes discriminant function results in that a covariance matrix of each category is adjusted based on the difference distribution. We evaluated the proposed method on multi-font texts and degraded texts such as compressed color images and faxed copies. We found that the recognition accuracy of our method for the evaluated texts was much higher than that of conventional methods.
机译:我们开发了一种学习伪贝叶斯判别方法,它动态地使伪贝叶斯判别函数与文本中存在的字体和图像劣化条件相适应。在该方法中,字符图案变形的特性表示为差异分布的统计信息,并且由差分分布表示的信息集成到伪贝叶斯判别函数中。将差分分布集成到伪贝内斯判别函数中的形成导致每个类别的协方差矩阵基于差异分布调整。我们在多字体文本上评估了所提出的方法,并降级文本,如压缩彩色图像和传真副本。我们发现,我们对评估文本的方法的识别准确性远高于传统方法的识别准确性。

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