Character shape reconstruction in video is challenging due to low contrast, complex backgrounds and arbitrary orientation of characters. This work proposes an Improved Ring Radius Transform (IRRT) for reconstructing impaired characters through medial axis prediction. At first, the technique proposes a novel idea based on the Tangent Vector (TV) concept that identifies each actual pair of end pixels caused by gaps in impaired character components. Next, the actual direction to predict medial axis pixels using IRRT for each pair of end pixels is proposed with a new normal vector concept. The process of prediction repeats iteratively to find all the medial axis pixels for every gap in question. Further, medial axis pixels with their radii are used to reconstruct the shapes of impaired characters. The proposed technique is tested on benchmark datasets consisting of video, natural scenes, objects and multi-lingual data to demonstrate that it reconstructs shapes well, even for heterogeneous data. Comparative studies with different binarization and character recognition methods show that the proposed technique is effective, useful and outperforms existing methods.

中文翻译：

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改进的基于环形半径变换的视频字符识别重建

由于低对比度、复杂的背景和字符的任意方向，视频中的字符形状重建具有挑战性。这项工作提出了一种改进的环形半径变换（IRRT），用于通过中轴预测重建受损字符。首先，该技术提出了一种基于切线向量 (TV) 概念的新颖想法，该概念可识别由受损字符组件中的间隙引起的每一对实际末端像素。接下来，使用新的法线向量概念提出了使用 IRRT 对每对末端像素预测中轴像素的实际方向。预测过程反复重复，以找到每个有问题的间隙的所有中轴像素。此外，具有半径的中轴像素用于重建受损字符的形状。所提出的技术在由视频、自然场景、对象和多语言数据组成的基准数据集上进行了测试，以证明它可以很好地重建形状，即使对于异构数据也是如此。不同二值化和字符识别方法的比较研究表明，所提出的技术是有效的、有用的并且优于现有的方法。