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%.
翻译:自然场景中的文字检测是计算机视觉和文件分析中的一个重要和积极的研究课题,因为其应用范围广泛,表现在 " 强力阅读竞赛 " 的出现。在上述竞争中具有良好的文字检测性表现的算法之一是 " 短信检测区域认识 " (CRAFT)。利用ICDAR2013年数据集,这项研究调查了自动图像分类和盲分变作为图像处理前步骤的影响,以进一步加强CRAFT的文字检测性能,从而进一步提高CRAFT的图像检测性能。拟议的技术通过使用一个有100个阈值的Laplacian操作器,将现场图像自动分为两类,即模糊和非螺旋。在应用CRAFT算法之前,被归类为模糊的图像是进一步预先处理的,使用盲分解法来减少模糊性。结果显示,拟议的方法大大提高了CRAFT的检测性能,如其IOU h-比例为94.47%,比CRAFT原91.42% h-比例,而这一比例为93.62%。