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Constructing high-quality character image datasets is challenging becausereal-world images are often affected by image degradation. There arelimitations when applying current image restoration methods to such real-worldcharacter images, since (i) the categories of noise in character images aredifferent from those in general images; (ii) real-world character imagesusually contain more complex image degradation, e.g., mixed noise at differentnoise levels. To address these problems, we propose a real-world characterrestoration network (RCRN) to effectively restore degraded character images,where character skeleton information and scale-ensemble feature extraction areutilized to obtain better restoration performance. The proposed method consistsof a skeleton extractor (SENet) and a character image restorer (CiRNet). SENetaims to preserve the structural consistency of the character and normalizecomplex noise. Then, CiRNet reconstructs clean images from degraded characterimages and their skeletons. Due to the lack of benchmarks for real-worldcharacter image restoration, we constructed a dataset containing 1,606character images with real-world degradation to evaluate the validity of theproposed method. The experimental results demonstrate that RCRN outperformsstate-of-the-art methods quantitatively and qualitatively.

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction