Locality Sensitive Pseudo-Code for Document Images
Locality Sensitive Pseudo-Code for Document Images
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DOI：
10.1109/icdar.2007.160
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
Kengo Terasawa;Yuzuru Tanaka
中科院分区：
文献类型：
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作者：
Kengo Terasawa;Yuzuru Tanaka
在本文中，我们提出了一种新的表示扫描文档中字符串图像的方案。我们将传统的多维描述符转换为伪码，伪码的性质是：如果两个向量在原始空间中接近，则编码的伪码以高概率半等价。为了实现这种转换，我们结合了局部敏感散列(LSH)索引，同时我们还开发了一族新的LSH函数，当所有向量都限制在单位球面上时，它优于以前的函数。基于我们的伪码的单词识别方法比基于多维描述符的方法更快，但几乎不降低准确率。
In this paper, we propose a novel scheme for representing character string images in the scanned document. We converted conventional multi-dimensional descriptors into pseudo-codes which have a property that: if two vectors are near in the original space then encoded pseudo-codes are 'semi equivalent with high probability. For this conversion, we combined locality sensitive hashing (LSH) indices and at the same time we also developed a new family of LSH functions that is superior to earlier ones when all vectors are constrained to lie on the surface of the unit sphere. Word spotting based on our pseudo-code becomes faster than multi-dimensional descriptor-based method while it scarcely degrades the accuracy.