Please use this identifier to cite or link to this item: http://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3882
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMukarambi G
dc.contributor.authorDhandra B.V
dc.contributor.authorMallappa S.
dc.date.accessioned2020-06-12T15:01:56Z-
dc.date.available2020-06-12T15:01:56Z-
dc.date.issued2019
dc.identifier.citationInternational Journal of Recent Technology and Engineering , Vol. 8 , 2 , p. 2988 - 2994en_US
dc.identifier.uri10.35940/ijrte.B2713.078219
dc.identifier.urihttp://gukir.inflibnet.ac.in:8080/jspui/handle/123456789/3882-
dc.description.abstractMost of the documents in various application areas like Government, Business and Research are available in the form of bi-lingual/multi-lingual text document. The multilingual documents are captured from video/camera for identification of script of the text document for automatic reading and editing. In this paper, an attempt is made to address the problem of script identification from camera captured document images using SFTA features. The input image is decomposed into a group of binary images by applying TTBD with fixing the number of the threshold as nt =3 empirically, on each decomposed binary image, Box Count, Mean Gray Level, and Pixel Count are extracted to form the feature vector. This feature vector is submitted to K-NN classifier to identify the scripts of the input document image. In all 10 scripts of the Indian languages are considered along with common English language as bi-lingual documents. The novelty of the paper is that 7 features are selected as potential features to obtain the highest accuracy. Features like Box Count (3), Mean Gray Level (2), and Pixel Count (2) have obtained the 87.02% recognition accuracy for English and Hindi Script combinations for the collected dataset and encouraging results for other combinations. These 7 potential features were selected using the technique named as feed-forward feature selection, from the set all 18 features. © BEIESP.en_US
dc.publisherBlue Eyes Intelligence Engineering and Sciences Publication
dc.subjectKNN
dc.subjectLBP
dc.subjectSFTA
dc.subjectSVM
dc.subjectTTBD
dc.titleCamera-based Bi-lingual script identification at word level using SFTA featuresen_US
dc.typeArticle
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.