Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156999
Title: Training deep network models for accurate recognition of texts in scene images
Authors: Zhang, Weilun
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Zhang, W. (2022). Training deep network models for accurate recognition of texts in scene images. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156999
Project: PSCSE20-0069
Abstract: With the advent of the industrial 4.0 era, computer vision and its applications have played an increasingly significant role in the digitization of industries. Images that contain text can be used as sources of information for automating processes. Text recognition from scene images constitutes still a very challenging task because of various backgrounds, variations in size and space, as well as irregular arrangements. The first half of this paper will study and analyse a deep learning model for word recognition in scene images using deep learning. As part of this work, CRNN, one of the most extensively used state-of-the-art deep learning models for text recognition, will be implemented and trained. A number of hyperparameters will be tweaked to obtain optimum performance. In addition, further improvements to CRNN recognition accuracy will be explored. Different image super-resolution techniques and the ImageFilter models will be utilized and compared as a pre-processing stage for recognition in the second part of this paper.
URI: https://hdl.handle.net/10356/156999
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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