文本定位与提取中的字符笔画检测

Krishna Subramanian, P. Natarajan, M. Decerbo, D. Castañón
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引用次数: 48

摘要

本文提出了一种新的图像分析方法,用于文本定位和提取。我们的方法对文本的字体、大小和颜色几乎没有限制,并且能够很好地处理场景文本和人工文本。本文利用文本的两个众所周知的特征:近似恒定笔画宽度和局部对比度,开发了一种快速、简单、有效的笔画检测算法。我们还展示了如何使用这些方法进行准确的提取,并激发了使用这种方法进行文本定位比其他基于颜色空间分割的方法的一些优点。我们分析了我们的笔划检测算法在ICDAR 2003鲁棒阅读竞赛中收集的图像上的性能。
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Character-Stroke Detection for Text-Localization and Extraction
In this paper, we present a new approach for analysis of images for text-localization and extraction. Our approach puts very few constraints on the font, size and color of text and is capable of handling both scene text and artificial text well. In this paper, we exploit two well-known features of text: approximately constant stroke width and local contrast, and develop a fast, simple, and effective algorithm to detect character strokes. We also show how these can be used for accurate extraction and motivate some advantages of using this approach for text localization over other color-space segmentation based approaches. We analyze the performance of our stroke detection algorithm on images collected for the robust-reading competitions at ICDAR 2003.
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