TY - JOUR A2 - Ahmad, Iftikhar AU - Al-Dabbagh, Mohammed Mumtaz AU - Salim, Naomie AU - Rehman, Amjad AU - Alkawaz, Mohammed Hazim AU - Saba, Tanzila AU - Al-Rodhaan, Mznah AU - Al-Dhelaan, Abdullah PY - 2014 DA - 2014/09/17 TI - Intelligent Bar Chart Plagiarism Detection in Documents SP - 612787 VL - 2014 AB - This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts. SN - 2356-6140 UR - https://doi.org/10.1155/2014/612787 DO - 10.1155/2014/612787 JF - The Scientific World Journal PB - Hindawi Publishing Corporation KW - ER -