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A medial axis based thinning strategy and structural feature extraction of character images

机译:基于中间轴的细化策略和字符图像结构特征提取

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The thinning methodology is novel in terms of its ability to incorporate character shape specific knowledge while constructing the thinned skeleton. But removal of spurious strokes or shape deformation in thinning is a difficult problem. In this paper, we have proposed a novel medial-axis based thinning strategy used for performing skeletonization of noisy character images. The proposed algorithm produces segmented strokes in vector form as a by-product. Hence further stroke segmentation is not required. Experiment is done on printed English, Bengali, Hindi, and Tamil characters and we obtain less spurious branches compared to other thinning methods without any post processing. We have concluded with a proposed methodology to extract structural features from thinned character images. This feature set improves the performance of existing OCR for Indian languages.
机译:细化方法就其在构造细化的骨架时结合字符形状特定知识的能力而言是新颖的。但是消除变薄的笔画或形状变薄是一个困难的问题。在本文中,我们提出了一种新颖的基于中轴的细化策略,用于执行噪声字符图像的骨架化。所提出的算法以向量形式产生分段的笔划作为副产物。因此,不需要进一步的笔划分割。实验是在印刷的英语,孟加拉语,北印度语和泰米尔语字符上进行的,与没有任何后期处理的其他细化方法相比,我们获得的伪分支更少。我们以提出的方法论作为结论,该方法论是从稀疏字符图像中提取结构特征。此功能集提高了现有印度语言OCR的性能。

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