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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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Braille Recognition Using Deep Learning

Li, C; Yan, W
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Conference contribution (739.3Kb)
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http://hdl.handle.net/10292/14743
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Abstract
Text 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.
Keywords
Braille recognition; Deep learning; Convolutional neural network; Natural scene text detection
Date
August 13, 2021
Source
In 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
Item Type
Conference Contribution
Publisher
ACM
DOI
10.1145/3484274.3484280
Publisher's Version
https://dl.acm.org/doi/10.1145/3484274.3484280
Rights Statement
© 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).

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