Abstract
Text segmentation in digital images is requisite for many image analysis and interpretation tasks. In this article, we have proposed an effective binarization method towards text segmentation in digital images. This method produces a number of connected components consisting of text as well as non-text. Next, it is required to identify the possible text components from the obtained connected components. Further, to distinguish between text and non-text components, a set of features are identified. Then, during training, we consider the two feature files namely text and non-text prepared by us. Here, K-Nearest Neighbour (K-NN) classifier is considered for the present two class classification problem. The experiments are based on ICDAR 2011 Born Digital Dataset. We have accomplished in binarization and as well as segmenting between text and non-text.
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Ghoshal, R., Das, S., Saha, A. (2017). K-NN Based Text Segmentation from Digital Images Using a New Binarization Scheme. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_17
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DOI: https://doi.org/10.1007/978-981-10-6430-2_17
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