Title Uniqueness Verification System Using NLP for Ensuring Originality and Compliance

Abstract: Ensuring the originality and appropriateness of titles is crucial in academic research, project submissions, and business naming. The Title Uniqueness Verification System leverages Natural Language Processing (NLP) to provide an automated solution for title validation. Developed as a Python Flask-based web application, the system allows users to register, log in, and submit titles through an intuitive interface. Upon submission, the system employs the Cosine Similarity algorithm to compare the submitted title with a dataset of existing titles. If the similarity score exceeds a predefined threshold, the title is rejected, preventing duplication and potential plagiarism. Additionally, the system integrates a keyword filtering mechanism to identify and reject titles containing disallowed or restricted words, ensuring compliance with specific content standards. This real-time, automated verification method helps researchers, academic institutions, and organizations maintain originality and adhere to content guidelines, significantly reducing redundancy and enhancing the integrity of title submissions.

Keywords: Title Verification, Natural Language Processing (NLP), Cosine Similarity, Plagiarism Detection, Keyword Filtering, Flask Web Application, Title Originality, Automated Title Validation, Academic Integrity, Content Compliance.