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@article{161401,
        author = {Ravindra. P. Shelkikar and S. M. Jagade},
        title = {Moth-Flame Optimization Algorithm for Indian Vehicle License Plate Characters Extraction and Recognition with Deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {3},
        pages = {587-593},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=161401},
        abstract = {License Plate Extraction and Recognition (LPER) finds numerous applications in intelligent transport systems. The LPR process comprises three primary steps: License Plate (LP) Extraction, segmentation, and classification. Each step requires specific techniques tailored to real-world conditions, each possessing unique characteristics. The LP extraction techniques identify the license plate, followed by segmentation algorithms that separate and isolate individual characters. Lastly, the classification step is employed to recognize the segmented characters. The overall accuracy of the process is reliant on the accuracy achieved at each step. To enhance the performance of the classification step, we propose a combined approach, integrating segmentation and classification into a single-stage process using deep learning techniques such as hybridization of Mothfly and Black Widow Optimization (HM-BWO) with Alexnet. Our experimental results demonstrate that this approach achieves a remarkable accuracy of 97.2% in recognizing Indian license plate characters, surpassing previous works.},
        keywords = {deep learning, artificial neural network, License plate extraction and recognition (LPER)},
        month = {},
        }