对比多种图像预处理方法，提升车牌识别准确率。
Comparison of Image Preprocessing Techniques for Vehicle License Plate Recognition Using OCR: Performance and Accuracy Evaluation
- 测试灰度化、CLAHE、双边滤波等预处理技术组合效果。
- 在巴西车牌数据集上，组合方法使识别准确率显著提升。
- 适合需要高精度车牌识别的智能交通系统开发者。
人工智能应用激增导致图像采集量大增，但图像质量缺乏标准化，影响模型性能。光学字符识别（OCR）常作为预处理手段，却在光照不足、分辨率低、透视畸变场景下表现不佳。本文评估了灰度转换、RGB空间中的CLAHE、双边滤波等预处理技术，单独及组合使用时的效果。采用准确率、精确率、召回率、F1分数、ROC曲线、AUC值和ANOVA分析，基于巴西车牌数据集进行验证。研究揭示了最优预处理策略，为实际场景中优化OCR性能提供依据。
原文摘要 · Abstract (English)
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.
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