  Text detection in natural scenes has been a significant and active research
subject in computer vision and document analysis because of its wide range of
applications as evidenced by the emergence of the Robust Reading Competition.
One of the algorithms which has good text detection performance in the said
competition is the Character Region Awareness for Text Detection (CRAFT).
Employing the ICDAR 2013 dataset, this study investigates the impact of
automatic image classification and blind deconvolution as image pre-processing
steps to further enhance the text detection performance of CRAFT. The proposed
technique automatically classifies the scene images into two categories, blurry
and non-blurry, by utilizing of a Laplacian operator with 100 as threshold.
Prior to applying the CRAFT algorithm, images that are categorized as blurry
are further pre-processed using blind deconvolution to reduce the blur. The
results revealed that the proposed method significantly enhanced the detection
performance of CRAFT, as demonstrated by its IoU h-mean of 94.47% compared to
the original 91.42% h-mean of CRAFT and this even outperformed the top-ranked
SenseTime, whose h-mean is 93.62%.
