  Removing undesirable specular highlight from a single input image is of
crucial importance to many computer vision and graphics tasks. Existing methods
typically remove specular highlight for medical images and specific-object
images, however, they cannot handle the images with text. In addition, the
impact of specular highlight on text recognition is rarely studied by text
detection and recognition community. Therefore, in this paper, we first raise
and study the text-aware single image specular highlight removal problem. The
core goal is to improve the accuracy of text detection and recognition by
removing the highlight from text images. To tackle this challenging problem, we
first collect three high-quality datasets with fine-grained annotations, which
will be appropriately released to facilitate the relevant research. Then, we
design a novel two-stage network, which contains a highlight detection network
and a highlight removal network. The output of highlight detection network
provides additional information about highlight regions to guide the subsequent
highlight removal network. Moreover, we suggest a measurement set including the
end-to-end text detection and recognition evaluation and auxiliary visual
quality evaluation. Extensive experiments on our collected datasets demonstrate
the superior performance of the proposed method.
