The proliferation of digital content has significantly increased the risk of plagiarism, creating challenges for maintaining academic integrity across various fields. This project introduces a robust document similarity analysis system designed to combat this issue effectively. Utilizing advanced text processing techniques such as tokenization, stemming, and shingle generation, the system efficiently identifies overlapping content between documents. Central to its functionality is the Rabin-Karp algorithm, which allows for precise string matching, enabling the detection of similarities even in paraphrased or restructured sentences. The user-friendly interface facilitates easy document uploads, providing detailed similarity reports that highlight matched content and offer insights into the degree of similarity detected. These reports serve as both a plagiarism detection tool and an educational resource, promoting proper citation practices and the importance of originality. By fostering a culture of ethical writing and originality, this system represents a significant advancement in the fight against plagiarism, empowering users to uphold academic integrity in an increasingly digital world.