Abstract
In this paper we have proposed a technique for skew detection and correction for printed documents, and have used an existing Optical Character Recognition (OCR) to recognize the characters. The proposed algorithm has the following steps (a) Applying the morphological dilations by defining the various structure elements (SE) (b) extracting the longest connected components (CC) (c) finding the global skew angle by statistical analysis of connected component (d) reference text line estimation and regression line fit to rotate the individual line by estimated angle of rotation. We have conducted experiment using printed images having different languages i.e. English, Devanagari, and Arabic (custom dataset) and have achieved significant performance.
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Mohammed, S.W., Soora, N.R. (2018). Global Skew Detection and Correction Using Morphological and Statistical Methods. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_48
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DOI: https://doi.org/10.1007/978-3-319-71767-8_48
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