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An optimization for binarization methods by removing binary artifacts

Raúl Rojas, Marte Ramirez-Ortegon, Volker Märgner, Erik Cuevas— 2013

In this article, we introduce a novel technique to remove binary artifacts. Given a gray-intensity image and its corresponding binary image, our method detects and remove connected components that are more likely to be background pixels. With this aim, our method constructs an auxiliary image by the min-imum-error-rate threshold and, then, computes the ratio of intersection between the connected compo-nents of the original binary image and the connected components of the auxiliary image. Connected components with high ratio are considered true connected components while the rest are removed from the output. We tested our method in binarization methods for historical documents (handwritten and printed). Our results are favorable and indicate that our method can improve the outputs from diverse binarization methods. In particular, a high improvement was observed for printed documents. Our method is easy to implement, has a moderate computational cost, and has two parameters whose model interpretation allows an easy empirical selection.

TitelAn optimization for binarization methods by removing binary artifacts
VerfasserRaúl Rojas, Marte Ramirez-Ortegon, Volker Märgner, Erik Cuevas
ThemaHistorical documents, threshold, denoising, binarization minimum error rate, Bayes theory
Datum201312
Quelle/n
Erschienen inPattern Recognition Letters, Vol. 34(11)
Größe oder Längepp. 1299-1306