Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140249
Title: Scene text recognition & vehicle license plate recognition
Authors: Hu, Wen Yang
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2020
Publisher: Nanyang Technological University
Abstract: Scene text recognition and vehicle license plate recognition both fall into the same class of computer vision problem: text recognition. Text recognition tackles the problem where characters are recognized in sequence and the length of the characters is varying. Although many recent works are proposed to improve the performance of text recognizer, there still remains a research gap on the tradeoff between the recognition accuracy and the inference speed. This project focus on the novel algorithm design on scene text recognition and the vehicle license plate recognition practice on real-world applications. Guided Training of Connectionist Temporal Classification (GTC) is proposed to achieve effective and efficient recognition. Graph Convolutional Network (GCN) is also introduced to further improve the performance. Experimental results show that my approach achieves a new state-of-the-art accuracy on most scene text recognition benchmarks and it shows great robustness on vehicle license plate recognition on real world images. A software pipeline is also made to recognize arbitrary text. Our method was even accepted as a poster in Conference AAAI 2020.
URI: https://hdl.handle.net/10356/140249
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
HWY_FYP_Report.pdf
  Restricted Access
1.91 MBAdobe PDFView/Open

Page view(s)

252
Updated on Jan 28, 2023

Download(s) 50

23
Updated on Jan 28, 2023

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.