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Multi-Font Rotated Character Recognition Using Periodicity

机译:基于周期性的多字体旋转字符识别

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This paper presents on accuracy improvement of multi-font rotated character recognition. Until now, a recognition method for rotated characters was based on distance criterion on the eigen sub-space. That is, an unknown pattern is projected onto the eigen-subspace of each category. The category which shows the closest distance between the projected point and the category locus is chosen. However, this simple method could not be cope with multi-font characters. Therefore, some unknown patterns were created by rotating the input pattern and projected onto the eigen-subspace of each category. By that method, a good performance was achieved for small size of categories like alphabetic 26 capital letters. However, the performance fell down by increasing the number of categories like 62 alpha-numeric letters. By considering the cause of the misclassification, we found that the distance criterion accidentally caused misclassification. This paper proposes a new feature based on periodic property of projected points on the eigen space. The experimental results showed a considerably high recognition rate.
机译:本文提出了多字体旋转字符识别的准确性提高。到目前为止,旋转字符的识别方法基于特征子空间上的距离标准。也就是说,将未知模式投影到每个类别的EIGEN子空间上。选择显示投影点与类别轨迹之间最近距离的类别。但是,这种简单的方法无法应对多字符字符。因此,通过旋转输入模式并投影到每个类别的eIgen子空间上来创建一些未知模式。通过该方法,为小型类别等字母26大写字母实现了良好的性能。但是,该性能通过增加62个字母数字字母等类别的数量来降低。通过考虑错误分类的原因,我们发现距离标准意外地造成了错误分类。本文提出了一种基于预测点对特征空间的周期性的新功能。实验结果表明了具有相当高的识别率。

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