Handwritten text segmentation using blurred image

Abstract : In this paper, we present our new method for the segmentation of handwritten text pages into lines, which has been submitted to ICDAR'2013 handwritten segmentation competition. This method is based on two levels of perception of the image: a rough perception based on a blurred image, and a precise perception based on the presence of connected components. The combination of those two levels of perception enables to deal with the difficulties of handwritten text segmentation: curvature, irregular slope and overlapping strokes. Thus, the analysis of the blurred image is efficient in images with high density of text, whereas the use of connected components enables to connect the text lines in the pages with low text density. The combination of those two kinds of data is implemented with a grammatical description, which enables to externalize the knowledge linked to the page model. The page model contains a strategy of analysis that can be associated to an applicative goal. Indeed, the text line segmentation is linked to the kind of data that is analysed: homogeneous text pages, separated text blocks or unconstrained text. This method obtained a recognition rate of more than 98% on last ICDAR'2013 competition.
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Submitted on : Tuesday, November 25, 2014 - 3:34:37 PM
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Aurélie Lemaitre, Jean Camillerapp, Bertrand Coüasnon. Handwritten text segmentation using blurred image. DRR - Document Recognition and Retrieval XXI, Jan 2014, San Francisco, United States. ⟨hal-01087210⟩

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