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Boosting Optical Character Recognition: A Super-Resolution Approach

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arxiv 1506.02211 v1 pith:E7NSJKZ2 submitted 2015-06-07 cs.CV

Chao Dong , Ximei Zhu , Yubin Deng , Chen Change Loy , Yu Qiao This is my paper
classification cs.CV
keywords super-resolutionimageimagestextcharacterentryicdar2015low-resolution
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Text-Aware Image Restoration with Diffusion Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    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.

  2. Task-driven real-world super-resolution of document scans

    cs.CV 2025-06 reject novelty 5.0 of 10

    Task-driven SR with OCR feature losses improves text-detection IoU on real scans but lowers PSNR, SSIM, and LPIPS relative to bicubic interpolation.

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