Text Similarity from Image Contents using Statistical and Semantic
  Analysis Techniques
Sagar Kulkarni Sharvari Govilkar Dhiraj Amin
Plagiarism detection is one of the most researched areas among the Natural
Language Processing(NLP) community. A good plagiarism detection covers all the
NLP methods including semantics, named entities, paraphrases etc. and produces
detailed plagiarism reports. Detection of Cross Lingual Plagiarism requires
deep knowledge of various advanced methods and algorithms to perform effective
text similarity checking. Nowadays the plagiarists are also advancing
themselves from hiding the identity from being catch in such offense. The
plagiarists are bypassed from being detected with techniques like paraphrasing,
synonym replacement, mismatching citations, translating one language to
another. Image Content Plagiarism Detection (ICPD) has gained importance,
utilizing advanced image content processing to identify instances of plagiarism
to ensure the integrity of image content. The issue of plagiarism extends
beyond textual content, as images such as figures, graphs, and tables also have
the potential to be plagiarized. However, image content plagiarism detection
remains an unaddressed challenge. Therefore, there is a critical need to
develop methods and systems for detecting plagiarism in image content. In this
paper, the system has been implemented to detect plagiarism form contents of
Images such as Figures, Graphs, Tables etc. Along with statistical algorithms
such as Jaccard and Cosine, introducing semantic algorithms such as LSA, BERT,
WordNet outperformed in detecting efficient and accurate plagiarism.