Boosting Optical Character Recognition: A Super-Resolution Approach
This work addresses a specific bottleneck in OCR systems for document analysis, but it is incremental as it builds on existing competition datasets and methods.
The paper tackled the problem of low-resolution text images degrading OCR performance by proposing a super-resolution framework, which improved OCR accuracy to 77.19%, close to the 78.80% achieved with high-resolution images.
Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset and the released Tesseract-OCR 3.02 system. We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images 78.80%.