Persian Plagirisim Detection Using CNN s

Lazemi, Soghra; Ebrahimpour-Komleh, Hossein; Noroozi, Nasser

doi:10.1109/iccke.2018.8566340

Cited by 3 publications

(1 citation statement)

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“…Flowchart images serve like inputs for the device and undergo preprocessing, wherein edges are detected using the 'Canny edge detection' algorithm. Subsequently, every shape within an image and bordersof the image are identified, and the Euclidean distance from the centroid to the boundary is computed, generating a graph [11]. This produced graph was then cross checks by graph of the actual image.…”

Section: Literature Surveymentioning

confidence: 99%

A Comprehensive Survey of Advanced Image Processing and OCR Techniques for Enhanced Image Plagiarism Detection

Palvadi Srinivas Kumar

2024

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In today's digital world, sharing files, documents, and presentations online has become routine for both work and study purposes. However, this surge in online sharing has brought forth a significant challenge: plagiarism. Plagiarism will happen whenever the copied content in various sources by not giving proper credit. This paper delves into the realm of plagiarism detection, particularly focusing on images. We discuss various tools and techniques designed to detect plagiarism and compare their effectiveness based on factors like accuracy. Finally, we analyze the work of different authors and share common findings. Our research employs a thorough review process to ensure the accuracy of our conclusions. By emphasizing the use of AI-powered tools, our agenda is to promote sharing of original dataover the online domain.

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Section: Literature Surveymentioning

confidence: 99%