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Degraded images commonly exist in the general sources of character images,leading to unsatisfactory character recognition results. Existing methods havededicated efforts to restoring degraded character images. However, thedenoising results obtained by these methods do not appear to improve characterrecognition performance. This is mainly because current methods only focus onpixel-level information and ignore critical features of a character, such asits glyph, resulting in character-glyph damage during the denoising process. Inthis paper, we introduce a novel generic framework based on glyph fusion andattention mechanisms, i.e., CharFormer, for precisely recovering characterimages without changing their inherent glyphs. Unlike existing frameworks,CharFormer introduces a parallel target task for capturing additionalinformation and injecting it into the image denoising backbone, which willmaintain the consistency of character glyphs during character image denoising.Moreover, we utilize attention-based networks for global-local featureinteraction, which will help to deal with blind denoising and enhance denoisingperformance. We compare CharFormer with state-of-the-art methods on multipledatasets. The experimental results show the superiority of CharFormerquantitatively and qualitatively.

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising