Sparsity-based super-resolution for offline handwriting recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Text image deblurring using text-specific properties
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Hi-index | 0.00 |
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 with 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.