Benchmarking Algorithms for Automatic License Plate Recognition

TL;DR

This paper benchmarks a lightweight CNN called LPRNet for automatic license plate recognition, comparing it with Tesseract across real and synthetic datasets, highlighting its robustness and potential for regional adaptation.

Contribution

It evaluates LPRNet's performance on real and synthetic datasets, providing detailed analysis and comparison with Tesseract, and suggests future transfer learning applications.

Findings

LPRNet achieved 90% and 89% accuracy on real and synthetic datasets.

Tesseract achieved 93% accuracy on synthetic data after pre-processing.

Pareto analysis identified most conflicting characters in license plates.

Abstract

We evaluated a lightweight Convolutional Neural Network (CNN) called LPRNet [1] for automatic License Plate Recognition (LPR). We evaluated the algorithm on two datasets, one composed of real license plate images and the other of synthetic license plate images. In addition, we compared its performance against Tesseract [2], an Optical Character Recognition engine. We measured performance based on recognition accuracy and Levenshtein Distance. LPRNet is an end-to-end framework and demonstrated robust performance on both datasets, delivering 90 and 89 percent recognition accuracy on test sets of 1000 real and synthetic license plate images, respectively. Tesseract was not trained using real license plate images and performed well only on the synthetic dataset after pre-processing steps delivering 93 percent recognition accuracy. Finally, Pareto analysis for frequency analysis of misclassified characters allowed us to find in detail which characters were the most conflicting ones according to the percentage of accumulated error. Depending on the region, license plate images possess particular characteristics. Once properly trained, LPRNet can be used to recognize characters from a specific region and dataset. Future work can focus on applying transfer learning to utilize the features learned by LPRNet and fine-tune it given a smaller, newer dataset of license plates.