Bayesian super-resolution of text in videowith a text-specific bimodal prior
| Autor: | Katherine Donaldson, Gregory K. Myers |
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| Rok vydání: | 2005 |
| Předmět: |
Computer science
business.industry Computer Science::Information Retrieval Bayesian probability ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Optical character recognition computer.software_genre Legibility Superresolution Readability Computer Science Applications Computer Science::Computer Vision and Pattern Recognition Pattern recognition (psychology) ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Range (statistics) Bicubic interpolation Computer vision Computer Vision and Pattern Recognition Artificial intelligence business computer Software ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | International Journal of Document Analysis and Recognition (IJDAR). 7:159-167 |
| ISSN: | 1433-2825 1433-2833 |
| DOI: | 10.1007/s10032-004-0139-y |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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