A Unified Framework for Text Extraction and Plagiarism Detection in Image-Based Content Using OCR and NLP
DOI:
https://doi.org/10.48047/CU/54/01/132-141Keywords:
Image OCR, NLP for Plagiarism, Text-in-Image Analysis, Visual Plagiarism Detection, Automated Content Verification, Image-to-Text, Document Integrity, Content Originality, AI-Powered PlagiarismAbstract
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 practices.
Downloads
References
“Online Assignment Plagiarism Checker Using Machine Learning”,Babitha, Harshitha M,HindumathiA,Reshma Farhin J,ISSN (O) 2278-1021, ISSN (P) 2319-5940,Issue 4, April 2022.
“Extracting text from image document and displaying its related information”, K.N. Natei journal of Engineering Research and Application (ISSN : 2248-9622, Vol. 8, Issue5 (Part -V) May2018.
.J. Pradeep, E. Srinivasan and S. Himavathi, “Diagonal Based Feature Extraction For Handwritten Alphabets Recognition System Using Neural Network”, International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 1, Feb 2011.
“Text Recognition using image processing”, International journal of Advanced Research in Computer Science by Chowdhury Md Mizan, Tridib Chakraborty and Suparna Karmakar (Vol-8,No.5,MayJune 2017).
A. Chitra et al., “Plagiarism Detection Using Machine Learning-Based Paraphrase Recognizer,” Journal of Intelligent Systems, October 2014.
Sk. Mahaboob Basha et al., “Text and Image Plagiarism Detection,” 2022.
Senosy Arrish et al., “Shape-Based Plagiarism Detection for Flowchart Figures in Texts,” International Journal of Computer Science & Information Technology (IJCSIT), vol. 6, no. 1, February 2014.
Amirul S. Bin Ibrahin et al., “Plagiarism Detection of Images,” in Proceedings of the Student Conference on Research and Development (SCOReD), September 2020.
Samanta et al., "Analysis of perceptual hashing algorithms in image manipulation detection," Procedia Computer Science, vol. 185, 2021, pp. 203-212.
Kuruvila et al., "Flowchart plagiarism detection system: an image processing approach," Procedia Computer Science, vol. 115, 2017, pp. 533-540.
Wang Wen “Research on Plagiarism Identification of Digital Images,” 2007 Digital Media Arts.
Akshay S et al., "Image Plagiarism Detection using Compressed Images," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 8, June 2019, ISSN: 2278-3075.
Chowdhury et al., "Plagiarism: Taxonomy, Tools and Detection Techniques."
Senosy Arrish, et al., "Shape-Based Plagiarism Detection for Flowchart Figures in Texts," International Journal of Computer Science & Information Technology (IJCSIT), vol. 6, no. 1, February 2014.
Mohamed A. El-Rashidy, et al., "Reliable Plagiarism Detection System Based on Deep Learning Approaches," Neural Computing and Applications, vol. 34, 2022, pp. 18837–18858.
Sotak Jr et al., "The Laplacian-of-Gaussian kernel: a formal analysis and design procedure for fast, accurate convolution and full-frame output," Computer Vision, Graphics, and Image Processing, vol. 48, no. 2, 1989, pp. 147-189.
Nelli, Fabio, "Python data analytics with Pandas, NumPy, and Matplotlib," 2018.
Kanopoulos et al., "Design of an image edge detection filter using the Sobel operator," IEEE Journal of Solid-State Circuits, vol. 23, no. 2, 1988, pp. 358-367
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dr Srinivas Kumar Palvadi, Dr. Krishna Prasad (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.