Abstract
Skew corrected text lines in multi-oriented handwritten documents often contain words that are not properly aligned. Most segmentation algorithms fail to correctly segment skewed words into constituent characters. So, skew correction of words in a text line is as important as skew correction of a text line in a document. In the present work, we propose a method that uses linear curve fitting for estimating and correcting skew present in handwritten words. This method efficiently detects and corrects skew in four Indian languages, namely Bangla, Hindi, Marathi and Panjabi. The proposed method is able to handle skewed word images to an extent of \(\pm {50^{\circ }}\) and provides accurate result even when the \(m\bar{a}tr\bar{a}\) is discontinuous. We have compared our method with existing ones to show the efficacy of the proposed method.
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Acknowledgements
The authors wish to thank Deepika Gupta, Junior Research Fellow, Department of Computer Science and Engineering, IIT(ISM), Dhanbad, for collecting, organizing and providing the Marathi data set used in this work.
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Pramanik, R., Bag, S. (2018). Linear Regression-Based Skew Correction of Handwritten Words in Indian Languages. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_11
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