Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing

Siyuan Zhang

doi:10.47297/taposatWSP2633-456924.20250609

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Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing

6卷第9期

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Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing
Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing
1.School of Engineering, Royal Melbourne Institute of Technology, Melbourne, Victoria, 3000, Australia
2.College of Civil Aviation NUAA, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, 210000, China
2025年6卷第9期页码：118-124
纸质出版：2025-09-30
DOI：10.47297/taposatWSP2633-456924.20250609
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Siyuan Zhang. (2025). Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing. 科技理论与实践（英文版）, 6(9), 118-124.

Zhang Siyuan.Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing[J].Theory and Practice of Science and Technology,2025,06(09):118-124.
Siyuan Zhang. (2025). Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing. 科技理论与实践（英文版）, 6(9), 118-124. DOI： 10.47297/taposatWSP2633-456924.20250609.

Zhang Siyuan.Advancing License Plate Recognition in Complex Environments: A Hybrid Approach Integrating Deep Learning and Image Pre-Processing[J].Theory and Practice of Science and Technology,2025,06(09):118-124. DOI： 10.47297/taposatWSP2633-456924.20250609.

摘要

Abstract

Automatic License Plate Recognition (ALPR) is a critical technology in security surveillance and traffic management. While existing systems achieve high accuracy in constrained conditions

their performance significantly degrades in complex

unconstrained environments characterized by factors such as poor illumination

adverse Iather

motion blur

and varying plate standards. To address these issues

this paper proposes the ALPR box. This system combines deep learning models and advanced image preprocessing techniques to form a multi-level pipeline. Initially

the preprocessing module adopted techniques such as gamma correction and histogram equalization to improve the low-light environment. But their contrast is limited. A robust license plate detection network

based on an enhanced YOLOv7 architecture

is then used to localize plates with high precision amidst cluttered backgrounds. For character segmentation

I combine a projection-based method with a connected component analysis (CCA) refined by deep learning-based validation to handle characters and noise. Finally

recognition is performed by a Convolutional Recurrent Neural Network (CRNN) incorporating a Connectionist Temporal Classification (CTC) layer

which is inherently robust to sequence alignment issues. The system is evaluated on a challenging custom dataset comprising images captured under diverse real-world complexities and on public benchmarks. Experimental results demonstrate that the proposed framework achieves a recognition accuracy of 96.5% on my dataset and outperforms several baseline methods

confirming its efficacy and robustness for ALPR in complex environments.

关键词

Keywords

references

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