Comparison of Image Preprocessing Techniques for Vehicle License Plate Recognition Using OCR: Performance and Accuracy Evaluation

TL;DR

This paper evaluates various image preprocessing techniques for vehicle license plate recognition using OCR, analyzing their impact on accuracy and robustness in challenging conditions to identify optimal methods.

Contribution

It systematically compares multiple preprocessing methods and combinations to improve OCR accuracy for license plate recognition in diverse real-world scenarios.

Findings

Bilateral Filter improves OCR accuracy under low-light conditions.

Combining grayscale and CLAHE yields higher F1-scores.

Optimal preprocessing varies with image quality and environmental factors.

Abstract

The growing use of Artificial Intelligence solutions has led to an explosion in image capture and its application in machine learning models. However, the lack of standardization in image quality generates inconsistencies in the results of these models. To mitigate this problem, Optical Character Recognition (OCR) is often used as a preprocessing technique, but it still faces challenges in scenarios with inadequate lighting, low resolution, and perspective distortions. This work aims to explore and evaluate various preprocessing techniques, such as grayscale conversion, CLAHE in RGB, and Bilateral Filter, applied to vehicle license plate recognition. Each technique is analyzed individually and in combination, using metrics such as accuracy, precision, recall, F1-score, ROC curve, AUC, and ANOVA, to identify the most effective method. The study uses a dataset of Brazilian vehicle license plates, widely used in OCR applications. The research provides a detailed analysis of best preprocessing practices, offering insights to optimize OCR performance in real-world scenarios.