Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Renato Augusto Tavares

TL;DR

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, and identify the most effective method.

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.

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

TL;DR

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, and identify the most effective method.

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.

Paper Structure

This paper contains 16 sections, 8 equations, 13 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Research Methodology and Research Questions
  3. Statistical Evaluation Metrics Used
  4. Accuracy
  5. Precision
  6. Recall (Sensitivity)
  7. F1-score
  8. ROC Curve and AUC (Area Under the Curve)
  9. ANOVA
  10. Preprocessing Algorithms Used
  11. No Preprocessing - EasyOCR
  12. Grayscale - EasyOCR
  13. CLAHE in RGB - EasyOCR
  14. Bilateral Filter - EasyOCR
  15. Conclusion and Future Work
  16. ...and 1 more sections

Figures (13)

  • Figure 1: Systematic literature review stages.
  • Figure 2: The ROC curve was generated with an AUC (Area Under the Curve) of 1.0.
  • Figure 3: The arithmetic mean and median of the model's execution time
  • Figure 4: Gaussian Distribution of Execution Times
  • Figure 5: The ROC Curve (Receiver Operating Characteristic) and the AUC (Area Under the Curve) remained unchanged
  • ...and 8 more figures