Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 507, p.689-700 (2017)

ISBN:

9789811024702

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007375323&doi=10.1007%2f978-981-10-2471-9_67&partnerID=40&md5=8c659c5aa32630d7a8fe6c508cdac14e

Keywords:

Artificial intelligence, Character recognition, Classification (of information), Discrimination coefficient, Geometric feature, Handwritten words, Information retrieval systems, Information science, Optical character recognition, Pre-printed documents, Printed word, Text processing

Abstract:

Classification of handwritten and printed text in pre-printed documents enhances the performance of optical character recognition technologies. The objective of work presented lies in devising an approach to perform automatic classification of printed and handwritten text at word level, which is inherently found in pre-printed documents. The proposed work consists of three stages to perform the classification of printed and handwritten words in Telugu pre-printed documents. The stage one encompasses the feature computation from the segmented words, stage two determines text discrimination coefficient, and finally, the classification of printed and handwritten text using a decision model is accomplished in stage three. The statistical and geometrical moment features are computed with respect to the text block under consideration, and furthermore, these features are employed for determination of text discrimination coefficient. The results of experimentation are proved to be promising and robust with an accuracy of around 98.2 %. © Springer Science+Business Media Singapore 2017.

Notes:

cited By 0; Conference of 1st International Conference on Computational Intelligence and Informatics, ICCII 2016 ; Conference Date: 28 May 2016 Through 30 May 2016; Conference Code:187689

Cite this Research Publication

N. Sab Rani and Vasudev, Tb, “An unsupervised classification of printed and handwritten telugu words in pre-printed documents using text discrimination coefficient”, Advances in Intelligent Systems and Computing, vol. 507, pp. 689-700, 2017.

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