Acceso usuarios
In today's digital landscape, images frequently contain valuable textual information, including numbers, symbols, and other critical data. Accurate extraction and verification of this embedded text are essential, especially in academic and content-rich fields where originality is paramount. This paper introduces a novel approach to detecting plagiarism in text embedded within images. Our method utilizes state-of-the-art Optical Character Recognition (OCR) techniques, combined with advanced Natural Language Processing (NLP) and deep learning algorithms, to extract and analyze the text content. By comparing the extracted text against a vast repository of existing sources, our system can effectively identify potential plagiarism while accurately distinguishing between original and copied content. This innovative approach ensures that not only traditional text documents but also image-based content is rigorously examined for authenticity, significantly enhancing the reliability of plagiarism detection across various content formats. The proposed solution offers a robust and automated pipeline for image-based text extraction and plagiarism detection, with the potential to revolutionize academic integrity, legal proceedings, and content creation practice.
