A new image binarisation technique for segmentation of text from digital images Online publication date: Wed, 28-Apr-2021
by Ranjit Ghoshal; Sayan Das; Aditya Saha
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 19, No. 1, 2021
Abstract: Text segmentation in digital images is requisite for many image analysis and interpretation tasks. In this article, we have proposed an effective binarisation technique towards text segmentation from digital images. This image binarisation technique creates numerous texts as well as non-text connected components. Next, it is required to separate the possible text components from the obtained connected components. Further, to distinguish between text and non-text components, a set of features are considered. Then, during training, we consider the two feature files namely text and non-text prepared by us. Here, K-nearest neighbour (K-NN) and support vector machine (SVM) classifiers are considered for the present two class classification problem. The experiments are based on ICDAR 2011 born digital dataset. Our binarisation technique is also applied on publically available dataset street view text dataset (SVT), DIBCO 2009 and ICDAR 2011 Roust Reading Competition. We have accomplished in binarisation and as well as segmenting between text and non-text.
Online publication date: Wed, 28-Apr-2021
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com