Braille Recognition Using Deep Learning

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aut.relation.conferenceICCCV'21: 2021 4th International Conference on Control and Computer Visionen_NZ
aut.researcherYan, Wei Qi
dc.contributor.authorLi, Cen_NZ
dc.contributor.authorYan, Wen_NZ
dc.date.accessioned2021-11-28T23:55:22Z
dc.date.available2021-11-28T23:55:22Z
dc.date.copyright2021-08-13en_NZ
dc.date.issued2021-08-13en_NZ
dc.description.abstractText is the media to convey and transmit information. Braille is extremely important for vision impaired people to exchange information through reading and writing. A braille translator is crucial tool for aiding people to understand braille messages. In this paper, we implement character-based braille translator using ResNet, there are three versions of ResNet we implement for braille classifiers, including ResNet-18, ResNet-34, and ResNet-50. We also implement a word-based braille detector using a novel solution called Adaptive Bezier-Curve Network (ABCNet), which is a Scene Text Recognition (STR) method for detecting word-based text in natural scenes. A comparison is present to evaluate the performance of ABCNet.
dc.identifier.citationIn 2021 4th International Conference on Control and Computer Vision (ICCCV'21). Association for Computing Machinery, New York, NY, USA, 30–35. DOI:https://doi.org/10.1145/3484274.3484280
dc.identifier.doi10.1145/3484274.3484280en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14743
dc.publisherACMen_NZ
dc.relation.urihttps://dl.acm.org/doi/10.1145/3484274.3484280
dc.rights© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION (see Citation), (see Publisher’s Version).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectBraille recognition; Deep learning; Convolutional neural network; Natural scene text detection
dc.titleBraille Recognition Using Deep Learningen_NZ
dc.typeConference Contribution
pubs.elements-id444653
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Centre for Robotics & Vision
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Science, Technology, Engineering, & Mathematics Tertiary Education Centre
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/AP - Design and Creative Technologies
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