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A New Multi-modal Technique for Bib Number/Text Detection in Natural Images

机译:一种用于自然图像中号码号码/文本检测的新型多模态技术

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The detection and recognition of racing bib number/text, which is printed on paper, cardboard tag, or t-shirt in natural images in marathon, race and sports, is challenging due to person movement, non-rigid surface, distortion by non-illumination, severe occlusions, orientation variations etc. In this paper, we present a multi-modal technique that combines both biometric and textual features to achieve good results for bib number/text detection. We explore face and skin features in a new way for identifying text candidate regions from input natural images. For each text candidate region, we propose to use text detection and recognition methods for detecting and recognizing bib numbers/texts, respectively. To validate the usefulness of the proposed multi-modal technique, we conduct text detection and recognition experiments before text candidate region detection and after text candidate region detection in terms of recall, precision and f-measure. Experimental results show that the proposed multi-modal technique outperforms the existing bib number detection method.
机译:由于人的活动,不坚硬的表面,由于非运动引起的扭曲,检测和识别赛车围嘴编号/文本是在马拉松,比赛和运动中以自然图像印在纸,纸板标签或T恤上的,因此具有挑战性。照明,严重遮挡,方向变化等。在本文中,我们提出了一种多模式技术,该技术结合了生物特征和文本特征,以实现良好的围嘴数字/文本检测结果。我们以一种新的方式探索面部和皮肤特征,以从输入的自然图像中识别文本候选区域。对于每个文本候选区域,我们建议分别使用文本检测和识别方法来检测和识别围兜编号/文本。为了验证所提出的多模式技术的实用性,我们在文本候选区域检测之前和之后的文本候选区域检测方面,在召回率,精度和f度量方面进行了文本检测和识别实验。实验结果表明,所提出的多峰技术优于现有的围兜数量检测方法。

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