论文标题
自动图像分类和盲目卷积在改善工艺算法的文本检测性能方面的影响
Impact of Automatic Image Classification and Blind Deconvolution in Improving Text Detection Performance of the CRAFT Algorithm
论文作者
论文摘要
自然场景中的文本检测是计算机视觉和文档分析中的重要研究主题，因为其广泛的应用范围是强大的阅读竞赛的出现所证明的。在上述竞争中具有良好文本检测性能的算法之一是文本检测（CRAFT）的角色区域意识。该研究采用ICDAR 2013数据集，研究了自动图像分类和盲目卷积的影响，作为图像预处理步骤，以进一步增强工艺的文本检测性能。提出的技术通过利用具有100个阈值的Laplacian操作员，将场景图像自动分为两个类别，模糊和非平移。在应用工艺算法之前，使用盲卷卷积将进一步预处理被归类为模糊的图像以减少模糊。结果表明，与原始的91.42％的H-均值相比，该方法显着提高了Craft的检测性能，其IOU H均值为94.47％，这甚至表现出了最高的Sensetime，其H均值为93.62％。
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%.
