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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Feature extraction by best anisotropic Haar bases in an OCR system
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Feature extraction by best anisotropic Haar bases in an OCR system

机译：通过OCR系统中的最佳各向异性Haar基地进行特征提取

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摘要

In this contribution, we explore the best basis paradigm for in feature extraction. According to this paradigm, a library of bases is built and the best basis is found for a given signal class with respect to some cost measure. We aim at constructing a library of anisotropic bases that are suitable for the class of 2-D binarized character images. We consider two, a dyadic and a non-dyadic generalization scheme of the Haar wavelet packets that lead to anisotropic bases. For the non-dyadic case, generalized Fibonacci p-trees are used to derive the space division structure of the transform. Both schemes allow for an efficient O(Nlog N) best basis search algorithm. The so built extended library of anisotropic Haar bases is used in the problem of optical character recognition. A special case, namely recognition of characters from very low resolution, noisy TV images is investigated. The best Haar basis found is then used in the feature extraction stage of a standard OCR system. We achieve very promising recognition rates for experimental databases of synthetic and real images separated into 59 classes.
机译：在此贡献中，我们探索了特征提取的最佳基础范例。根据这种范例，建立了一个基础库，并且就某种成本度量而言，找到了给定信号类别的最佳基础。我们旨在构建适用于二维二值化字符图像类的各向异性基础库。我们考虑了导致各向异性基数的Haar小波包的二元和非二元泛化方案。对于非二进角情况，使用广义Fibonacci p树来导出变换的空间划分结构。两种方案都允许有效的O（Nlog N）最佳基础搜索算法。如此建立的各向异性Haar基地的扩展库用于光学字符识别问题。研究了一种特殊情况，即从非常低的分辨率，嘈杂的电视图像中识别字符。然后，将找到的最佳Haar基础用于标准OCR系统的特征提取阶段。对于分为59类的合成图像和真实图像的实验数据库，我们获得了非常有希望的识别率。

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