Bayesian super-resolution of text in video with a text-specific bimodal prior

被引：15

作者：

Donaldson K. ^{[1
]}

Myers G.K. ^{[1
]}

机构：

[1] SRI International, Menlo Park, CA 94025

来源：

International Journal of Document Analysis and Recognition (IJDAR) | 2005年 / 7卷 / 2-3期

关键词：

OCR; Super-resolution; Video;

D O I：

10.1007/s10032-004-0139-y

中图分类号：

学科分类号：

摘要：

To increase the range of sizes of video scene text recognizable by optical character recognition (OCR), we developed a Bayesian super-resolution algorithm that uses a text-specific bimodal prior. We evaluated the effectiveness of the bimodal prior, compared and in conjunction with a piecewise smoothness prior, visually and by measuring the accuracy of the OCR results on the variously super-resolved images. The bimodal prior improved the readability of 4- to 7-pixel-high scene text significantly better than bicubic interpolation and increased the accuracy of OCR results better than the piecewise smoothness prior. © Springer-Verlag 2005.

引用

页码：159 / 167

页数：8

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