Ai-based Plagiarism Analyzer With Sentence-level Similarity Detection

The fast expansion of digital information and the ease of access to internet resources have made plagiarism a serious concern in academic, research, and content creation settings.Keyword matching and document-level similarity metrics are the mainstays of traditional plagiarism detection systems, which frequently fall short in identifying paraphrased or semantically identical text.In order to increase detection accuracy, this research study provides an AI-based plagiarism analyzer with sentence-level similarity detection that makes use of machine learning and natural language processing (NLP).In order to detect both straight copying and cleverly paraphrased language, the suggested method uses semantic similarity models to assess textual content at the sentence level.The system offers comprehensive similarity reports that show precise and contextual overlaps by combining methods including tokenization, vector embeddings, cosine similarity, and transformer-based language models.The suggested strategy greatly improves plagiarism detection performance when compared to conventional techniques, according to experimental evaluation, which makes it appropriate for educational institutions and digital content platforms.