IEEE Trans Image Process - Robust document image binarization technique for degraded document images.

Tópicos

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Resumo

Segmentation of text from badly degraded document images is a very challenging task due to the high inter/intra-variation between the document background and the foreground text of different document images. In this paper, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Canny's edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning. It has been tested on three public datasets that are used in the recent document image binarization contest (DIBCO) 2009 & 2011 and handwritten-DIBCO 2010 and achieves accuracies of 93.5%, 87.8%, and 92.03%, respectively, that are significantly higher than or close to that of the best-performing methods reported in the three contests. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other techniques.

Resumo Limpo

segment text bad degrad document imag challeng task due high interintravari document background foreground text differ document imag paper propos novel document imag binar techniqu address issu use adapt imag contrast adapt imag contrast combin local imag contrast local imag gradient toler text background variat caus differ type document degrad propos techniqu adapt contrast map first construct input degrad document imag contrast map binar combin canni edg map identifi text stroke edg pixel document text segment local threshold estim base intens detect text stroke edg pixel within local window propos method simpl robust involv minimum paramet tune test three public dataset use recent document imag binar contest dibco handwrittendibco achiev accuraci respect signific higher close bestperform method report three contest experi bickley diari dataset consist sever challeng bad qualiti document imag also show superior perform propos method compar techniqu

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