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@article{185646,
        author = {Pooja Ukey and Gauri Tantele and Mrunali Choudhari and Bharti Lakade},
        title = {Character Recognition on Vehicle Plate: An Experimental Research Paper},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2308-2310},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185646},
        abstract = {Automatic Vehicle License Plate Recognition (VLPR) has become an essential technology in Intelligent Transportation Systems (ITS). It provides an automated way to identify vehicles using image processing and machine learning techniques. The recognition system primarily involves three stages: license plate detection, character segmentation, and character recognition. This paper presents an experimental study on character recognition from vehicle license plates using machine learning models. The performance of different algorithms such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN) is compared to identify the most suitable approach for accurate recognition. The experimental results show that CNN-based methods outperform traditional approaches, achieving higher accuracy in noisy and distorted images.},
        keywords = {},
        month = {October},
        }