License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation

TL;DR

This paper introduces a deep learning-based system for license plate detection and recognition, achieving high accuracy across multiple datasets, and investigates how font features affect recognition performance to guide future improvements.

Contribution

It presents a dual deep learning approach combining Faster R-CNN and CNN-RNN with CTC loss for improved license plate detection and recognition, and analyzes font effects on recognition accuracy.

Findings

Recall rate of 92% on CENPARMI dataset

Font characteristics significantly impact recognition performance

Deep learning methods outperform traditional approaches

Abstract

License plate detection (LPD) is essential for traffic management, vehicle tracking, and law enforcement but faces challenges like variable lighting and diverse font types, impacting accuracy. Traditionally reliant on image processing and machine learning, the field is now shifting towards deep learning for its robust performance in various conditions. Current methods, however, often require tailoring to specific regional datasets. This paper proposes a dual deep learning strategy using a Faster R-CNN for detection and a CNN-RNN model with Connectionist Temporal Classification (CTC) loss and a MobileNet V3 backbone for recognition. This approach aims to improve model performance using datasets from Ontario, Quebec, California, and New York State, achieving a recall rate of 92% on the Centre for Pattern Recognition and Machine Intelligence (CENPARMI) dataset and 90% on the UFPR-ALPR dataset. It includes a detailed error analysis to identify the causes of false positives. Additionally, the research examines the role of font features in license plate (LP) recognition, analyzing fonts like Driver Gothic, Dreadnought, California Clarendon, and Zurich Extra Condensed with the OpenALPR system. It discovers significant performance discrepancies influenced by font characteristics, offering insights for future LPD system enhancements. Keywords: Deep Learning, License Plate, Font Evaluation