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dc.contributor.advisorYan, Wei Qi
dc.contributor.authorLi, Changjian
dc.date.accessioned2022-04-04T22:00:48Z
dc.date.available2022-04-04T22:00:48Z
dc.date.copyright2022
dc.identifier.urihttp://hdl.handle.net/10292/15042
dc.description.abstractIn recent years, deep learning methods have been applied to our daily lives and various industries. Visual object detection methods are broadly employed to a consortium of tasks, including human face detection in public areas, traffic signs detection, car plate number recognition, etc. Natural Language Processing (NLP) methods are implemented for language translation, Automatic Speech Recognition (ASR), client embedding, item embedding, etc. In this thesis, we contribute to special character recognition by using deep learning. The Adaptive Bezier Curve Network (ABCNet) is a text detection and recognition method utilized to recognize English Braille, which implements parameterized Bezier curves for detecting arbitrary-shape text in natural scenes. YOLOv5 is the second deep learning method that was implemented for Māori symbol recognition. The methods show outstanding performance in our experiments. Both methods detect and recognize visual objects with high accuracies. The results of our experiments prove deep learning methods are feasible to be implemented for detecting and classifying special characters, shortening the time cost of translation, and reducing labor costs.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectDeep learningen_NZ
dc.subjectObject detectionen_NZ
dc.subjectScene text recognition and detectionen_NZ
dc.subjectABCNeten_NZ
dc.subjectYOLOv5en_NZ
dc.titleSpecial Character Recognition Using Deep Learningen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2022-04-04T15:50:36Z


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