CNN-Based Character Recognition for License Plate Recognition System. Pham, V. H., Dinh, P. Q., & Nguyen, V. H. In Nguyen, N. T., Hoang, D. H., Hong, T., Pham, H., & Trawinski, B., editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10752 LNAI, pages 594–603, 2018. Springer. Series Title: Lecture Notes in Computer Science ISSN: 16113349![CNN-Based Character Recognition for License Plate Recognition System [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex License Plate Recognition is a practical use of computer vision based application. With the increase in demand of automation transportation systems, this application plays a very big role in the system development. Also, the use of vehicles has been increasing because of population growth and human needs in recent years makes the application is more challenging. Moreover, license plates are available in diverse colors and style and that the presence of noise, blurring in the image, uneven illumination, and occlusion makes the task even more difficult for conventional recognition methods. We propose an approach of using a Convolutional Neural Networks (CNN) classifier for the recognition. Pre-processing techniques are firstly applied on input images, such as filtering, thresholding, and then segmentation. Then, we train a CNN classifier for character recognition. Although the performance of a CNN is very impressive, it costs much time to complete the character recognition step. In this study, a modified CNN is proposed to help the system run in real-time. Experimental results have done and analyzed with other methods.
@inproceedings{DBLP:conf/aciids/PhamDN18,
title = {{CNN}-{Based} {Character} {Recognition} for {License} {Plate} {Recognition} {System}},
volume = {10752 LNAI},
isbn = {978-3-319-75419-2},
url = {https://doi.org/10.1007/978-3-319-75420-8%5C_56},
doi = {10.1007/978-3-319-75420-8_56},
abstract = {License Plate Recognition is a practical use of computer vision based application. With the increase in demand of automation transportation systems, this application plays a very big role in the system development. Also, the use of vehicles has been increasing because of population growth and human needs in recent years makes the application is more challenging. Moreover, license plates are available in diverse colors and style and that the presence of noise, blurring in the image, uneven illumination, and occlusion makes the task even more difficult for conventional recognition methods. We propose an approach of using a Convolutional Neural Networks (CNN) classifier for the recognition. Pre-processing techniques are firstly applied on input images, such as filtering, thresholding, and then segmentation. Then, we train a CNN classifier for character recognition. Although the performance of a CNN is very impressive, it costs much time to complete the character recognition step. In this study, a modified CNN is proposed to help the system run in real-time. Experimental results have done and analyzed with other methods.},
booktitle = {Lecture {Notes} in {Computer} {Science} (including subseries {Lecture} {Notes} in {Artificial} {Intelligence} and {Lecture} {Notes} in {Bioinformatics})},
publisher = {Springer},
author = {Pham, Van Huy and Dinh, Phong Quang and Nguyen, Van Huan},
editor = {Nguyen, Ngoc Thanh and Hoang, Duong Hung and Hong, Tzung-Pei and Pham, Hoang and Trawinski, Bogdan},
year = {2018},
note = {Series Title: Lecture Notes in Computer Science
ISSN: 16113349},
keywords = {Character recognition, Convolution Neural Network, License Plate Recognition System},
pages = {594--603},
}
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