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Typography and layout lead to the hierarchical organisation of text in words,text lines, paragraphs. This inherent structure is a key property of text inany script and language, which has nonetheless been minimally leveraged byexisting text detection methods. This paper addresses the problem of textsegmentation in natural scenes from a hierarchical perspective. Contrary toexisting methods, we make explicit use of text structure, aiming directly tothe detection of region groupings corresponding to text within a hierarchyproduced by an agglomerative similarity clustering process over individualregions. We propose an optimal way to construct such an hierarchy introducing afeature space designed to produce text group hypotheses with high recall and anovel stopping rule combining a discriminative classifier and a probabilisticmeasure of group meaningfulness based in perceptual organization. Resultsobtained over four standard datasets, covering text in variable orientationsand different languages, demonstrate that our algorithm, while being trained ina single mixed dataset, outperforms state of the art methods in unconstrainedscenarios.

A Fast Hierarchical Method for Multi-script and Arbitrary Oriented Scene Text Extraction