This chapter describes a new approach for the segmentation of text in images on Web pages. In the same spirit as the authors’ previous work on this subject, this approach attempts to model the ability of humans to differentiate between colours. In this case, pixels of similar colour are first grouped using a colour distance defined in a perceptually uniform colour space (as opposed to the commonly used RGB). The resulting colour connected components are then grouped to form larger (character-like) regions with the aid of a propinquity measure, which is the output of a fuzzy inference system. This measure expresses the likelihood for merging two components based on two features. The first feature is the colour distance between the components, in the L*a*b* colour space. The second feature expresses the topological relationship of two components. The results of the method indicate a better performance than previous methods devised by the authors and possibly better (a direct comparison is not really possible due to the differences in application domain characteristics between this and previous methods) performance to other existing methods.
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机译：本章介绍了一种用于对网页图像中的文本进行分割的新方法。与作者以前在该主题上的工作一样，这种方法试图模拟人类区分颜色的能力。在这种情况下，首先使用在感知上均匀的颜色空间（与常用的RGB相反）中定义的颜色距离对相似颜色的像素进行分组。然后，通过邻近度度量将所得的颜色连接的分量进行分组，以形成较大的（字符状）区域，该区域是模糊推理系统的输出。该度量表示基于两个特征合并两个组件的可能性。第一个特征是L * a * b *颜色空间中组件之间的颜色距离。第二个特征表示两个组件的拓扑关系。该方法的结果表明，其性能优于作者设计的先前方法，并且可能具有与其他现有方法相比更好的性能（由于该方法与先前方法之间的应用程序域特征不同，因此无法直接进行比较）。
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