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Graph-Based Background Suppression for Scene Text Detection

机译:用于场景文本检测的基于图的背景抑制

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摘要

Detecting text in video or natural scene image is quite challenging due to the complex background, various fonts and illumination conditions. The preprocessing period, which suppresses the nontext areas so as to highlight the text areas, is the basis for further text detection. In this paper, a novel graph-based background suppression method for scene text detection is proposed. Considering each pixel as a node in the graph, our approach incorporates pixel-level and context-level features into a graph. Various factors contribute to the unary and pair wise cost function which is optimized via max-flow/min-cut algorithm [16] to get a binary image whose nontext pixels are suppressed so that text pixels are highlighted. Furthermore, the proposed background suppression method could be easily combined with other detection methods to improve the performance. Experimental results on ICDAR 2011 competition dataset show promising performance.
机译:由于背景复杂,字体和照明条件复杂,在视频或自然场景图像中检测文本非常具有挑战性。预处理周期抑制了非文本区域以突出显示文本区域,这是进一步检测文本的基础。提出了一种基于图的背景图像场景文本检测新方法。将每个像素视为图形中的一个节点,我们的方法将像素级和上下文级功能合并到图形中。一元和成对成本函数受多种因素的影响,该函数通过最大流/最小剪切算法[16]进行了优化,以得到其非文本像素被抑制从而突出显示文本像素的二进制图像。此外,所提出的背景抑制方法可以容易地与其他检测方法结合以提高性能。 ICDAR 2011竞赛数据集的实验结果显示出令人鼓舞的性能。

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