Special Character Recognition Using Deep Learning
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In 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.