article · IEEE Access
A new methodology combines image processing, genetic algorithms, and neutrosophic sets to improve automatic vehicle licence plate recognition. The approach applies edge detection and morphological operations to locate plates, followed by a genetic algorithm that optimises neutrosophic operations to reduce image indeterminacy and extract key features. A k-means clustering algorithm then segments individual characters, which are extracted using connected components labelling analysis. Evaluated using a custom database, the system handles both Egyptian Arabic and English licence plates across different image conditions. In testing, the system achieved 96.67 percent accuracy on high-resolution Egyptian plates and 94.27 percent on degraded, low-resolution English plates. When tested against disturbances such as camera flash, external noise, and varying illumination, it achieved 92.5 percent accuracy, outperforming traditional approaches that achieved 79 percent.
Automatic licence plate recognition frequently struggles with poor image quality, lighting variations, and diverse language scripts. By reducing visual indeterminacy through optimised mathematical sets, this approach delivers dependable recognition across both Arabic and English text. This helps maintain high accuracy even when images suffer from low resolution, noise, or severe glare, which are common hurdles in real-world vehicle tracking.
The method is applicable to automated vehicle identification systems, traffic control, and parking monitoring services requiring bilingual character recognition. The technology sits at an applied and tested stage, having been validated against a dedicated test database featuring real-world image corruptions. Bringing this system to market would require integration with existing camera hardware, testing on video streams, and adaptation to real-time processing constraints.
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The present paper proposes a new methodology for license plate (LP) recognition in the state of the art of image processing algorithms and an optimized neutrosophic set (NS) based on genetic algorithm (GA). First of all, we have performed some image processing techniques such as edge detection and morphological operations in order to utilize the (LP) localization. In addition, we have extracted the most salient features by implementing a new methodology using (GA) for optimizing the (NS) operations. The use of (NS) decreases the indeterminacy on the (LP) images. Moreover, k-means clustering algorithm has been applied to segment the (LP) characters. Finally, we have applied connected components labeling analysis (CCLA) algorithm for identifying the connected pixel regions and grouping the appropriate pixels into components to extract each character effectively. Several performance indices have been calculated in order to measure the system efficiency such as accuracy, sensitivity, specificity, dice, and jaccard coefficients. Moreover, we have created a database for all detected and recognized (LP) for testing purposes. Experimental results show that the proposed methodology has the ability to be suitable for both (Arabic –Egyptian) and English (LP). The proposed system achieves high degree of recognition accuracy for the whole system according to the following case studies; (i) for a high resolution Egyptian (LP), the proposed system achieves about 96.67% accuracy of correct recognition, (ii) for a low resolution-corrupted English (LP), the proposed system achieves about 94.27% accuracy. In addition, we have applied the proposed system on some sort of image disturbance i.e. (flash in image, external noise, and illumination variation), the proposed system achieves about 92.5% accuracy of correct identification. However, traditional methods achieve about 79% accuracy of correct identification in the presence of such image degradations. This reflects how the proposed system is generalized, optimized, and proposes high degree of recognition accuracy.
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DOI: 10.1109/access.2020.2979185
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