An Enhanced Framework for Extrinsic Plagiarism Avoidance for Research Article

Abstract

Various approaches have been implemented for plagiarism detection used, for author‘s work and academic publication, there is a purpose to create such reliable and performant plagiarism detection with increasing amount of publications. This is a serious offense where one author presents someone else’s work as his own. Moreover these algorithms don’t consider similar sections for efficient comparison. The proposed framework performs efficient sections wise plagiarism detection and provides suggestions for improving documents. The precision, recall and accuracy based on different n-gram features is presented showing the strictness of higher level n-gram features.