Natural Language Processing-Based Plagiarism Detector - G. Harsha Vardhani; Sri. G. Kalyan Chakravarthi; Praveen Kumar j; M. Nishita; P. Satish (International Journal of Electrical and Communication Engineering Technology, 2024)
Plagiarism is the act of taking another's work and claiming it as his/her own without giving the credit. Plagiarism is a grave concern in many fields like education, literature, business, etc. The project aims for text and image plagiarism detection employed by a web application containing four modules. Text plagiarism is detected when the input is either text or PDF. For PDF, the text extraction is done from the PDF and then detected for plagiarism. Image plagiarism detection is performed by taking an image as input. Image plagiarism detection has an extension that takes input as an image containing text. The text extraction is done from the input and compared for similarity with the documents of the dataset and the output generated. Text plagiarism percentage detection uses Jaccard Similarity and Image plagiarism percentage detection uses Cosine Similarity. Image plagiarism detection uses Levenshtein Distance if the input is image-containing text. Additionally, some features are added to this project like Citation status detection, AI content detection, and Type of Plagiarism detection.
Keywords: Jaccard similarity; Plagiarism detection; cosine similarity; levenshtein distance; web application