Abstract
The examination of thesis diplomas for instances of plagiarism can be a difficult and time-consuming task. However, some parts of these documents are more important and are more susceptible to being plagiarized than others. This work investigates four distinct approaches to the detection and exclusion of such irrelevant fragments from the process of plagiarism detection to reduce the cognitive load inflicted upon supervisors. We evaluate the relevance of image and text data on solving this task by exploring models that are multimodal, purely image-based, purely text-based, and based on handcrafted features.
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Karaś, D., Śpiewak, M. (2024). The Uniform Anti-plagiarism System (JSA): The Role of Layout in Diploma Thesis Page Classification. In: Biele, C., et al. Digital Interaction and Machine Intelligence. MIDI 2023. Lecture Notes in Networks and Systems, vol 1076. Springer, Cham. https://doi.org/10.1007/978-3-031-66594-3_3
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DOI: https://doi.org/10.1007/978-3-031-66594-3_3
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