A diffusion restoration model jointly trained with a text-spotting module and prompted by its own recognized text improves text recognition accuracy on restored images compared with general-purpose restoration methods.
Boosting Optical Character Recognition: A Super-Resolution Approach
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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%.
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Text-Aware Image Restoration with Diffusion Models
A diffusion restoration model jointly trained with a text-spotting module and prompted by its own recognized text improves text recognition accuracy on restored images compared with general-purpose restoration methods.