Border detection of document images scanned from large books

Shamqoli, Maryam; Khosravi, Hossein

doi:10.1109/iranianmvip.2013.6779955

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Scanning of documents using vision alone without dedicated hardware presents a few challenges to be solved. One of them is correctly detecting the borders or the corners of documents in the images, as described in [5]. Whether it is to fix the skew or properly extract the document page from an image, detecting the edges of a document is essential.…”

Section: Introductionmentioning

confidence: 99%

Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots

et al. 2021

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The COVID-19 pandemic has had a significant impact worldwide, impacting schools, undergraduate, and graduate university education. More than half a million lives have been lost due to COVID-19. Moving towards contactless learning activities has become a research area due to the rapid advancement of technology, particularly in artificial intelligence and robotics. This paper proposes an autonomous service robot for handling multiple teaching assistant duties in the educational field to move towards contactless learning activities during pandemics. We use SLAM to map and navigate the environment to proctor an exam. We also propose a human–robot voice interaction and an academic content personalization algorithm. Our results show that our robot can navigate the environment to proctor students avoiding any static or dynamic obstacles. Our cheating detection system obtained a testing accuracy of 86.85%. Our image-based exam paper scanning system can scan, extract, and process exams with high accuracy.

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Section: Introductionmentioning

confidence: 99%