Research of Convolutional Neural Networks for Text Recognition in Traffic Scene Imagery

DOI

10.2991/978-94-6463-821-9_35How to use a DOI?

Keywords

Convolutional Neural Networks (CNNS); Traffic Scene Imagery; Intelligent Transportation Systems (ITS)

Abstract

In the era of rapid development of intelligent transportation systems (ITS), the ability to accurately recognize text in traffic scene imagery is of utmost significance. Traffic signs, license plates, and road information in these images are crucial for traffic management, law enforcement, and navigation. For autonomous driving, it is a fundamental requirement for vehicle decision - making. However, previous text recognition methods, which depend on hand - crafted features, struggle in complex traffic scenarios. This paper conducts an in - depth exploration of Convolutional Neural Networks (CNNs) in traffic scene text recognition. It first details the theoretical principles of CNNs, including the functions of each component in the network structure, the importance of image preprocessing, and the processes of text detection and feature recognition. Then, it showcases CNN - based research in the transportation field, particularly in traffic sign recognition. It presents the comparison of YOLOv5 and SSD models in terms of accuracy and speed, and highlights a Chinese traffic sign detection algorithm. Finally, the paper proposes future research directions such as dataset expansion and new model testing, aiming to strengthen the performance of text recognition in traffic scenes and play a role in promoting the evolution of intelligent transportation.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Quanqi Liu
PY  - 2025
DA  - 2025/08/31
TI  - Research of Convolutional Neural Networks for Text Recognition in Traffic Scene Imagery
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 325
EP  - 333
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_35
DO  - 10.2991/978-94-6463-821-9_35
ID  - Liu2025
ER  -