Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138112
Title: Training deep network models for accurate recognition of texts in scenes
Authors: Teo, Ren Jie
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2020
Publisher: Nanyang Technological University
Project: SCSE19-0046
Abstract: Deep learning has seen a resurgence in the machine learning community in the past decade. Research on scene text detection and recognition using deep learning allows for more innovation in solving current issues. Current solutions treat text recognition as a category to be researched separately and more could be done to improve on that,making it an end-to-end system. In this FYP, deep learning network models will be implemented to recognise texts in various scenes. Hyper parameters of the deep learning network models will be fine-tuned to achieve optimal performance. This will create a great learning and practical experience. For optimal performance, it may be necessary to give up test accuracy for training speed sometimes. Comparison will be made between the deep learning network model fine-tuned for optimal performance, and a recent state-of-the-art deep learning network model without fine-tuning. This shows the improvement in research on the subject area. However,without a text detection system working in tandem with the text recognition model,scene text recognition will not serve much real world use. Future work for this project recommends better hardware to allow for more room to work with when fine-tuning hyper parameters, and possible integration with another system to make the scene text recognition an end-to-end model.
URI: https://hdl.handle.net/10356/138112
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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