Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition
One of the major causes of poor results in license plate recognition is low quality of images affected by multiple factors, such as severe illumination condition, complex background, different weather conditions, night light, and perspective distortions. In this paper, we propose a new mathematical...
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2018
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| author | Raghunandan, K.S. Shivakumara, Palaiahnakote Jalab, Hamid Abdullah Ibrahim, Rabha Waell Kumar, Govindaraj Hemantha Pal, Umapada Lu, Tong |
| author_facet | Raghunandan, K.S. Shivakumara, Palaiahnakote Jalab, Hamid Abdullah Ibrahim, Rabha Waell Kumar, Govindaraj Hemantha Pal, Umapada Lu, Tong |
| author_sort | Raghunandan, K.S. |
| collection | UM |
| description | One of the major causes of poor results in license plate recognition is low quality of images affected by multiple factors, such as severe illumination condition, complex background, different weather conditions, night light, and perspective distortions. In this paper, we propose a new mathematical model based on Riesz fractional operator for enhancing details of edge information in license plate images to improve the performances of text detection and recognition methods. The proposed model performs convolution operation of the Riesz fractional derivative over each input image by enhancing the edge strength in it. To test the performance of the proposed model, we conduct experiments on benchmark license plate image databases, namely, UCSD and ICDAR 2015-SR competition text image databases. Experimental results on enhancement show that the proposed model outperforms the existing baseline enhancement techniques in terms of quality measures. Furthermore, experimental results on text detection and recognition show that text detection and recognition rates are improved significantly after enhancement compared with before enhancement. |
| first_indexed | 2024-03-06T05:52:51Z |
| format | Article |
| id | um.eprints-20991 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:52:51Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | um.eprints-209912019-04-18T02:17:05Z http://eprints.um.edu.my/20991/ Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition Raghunandan, K.S. Shivakumara, Palaiahnakote Jalab, Hamid Abdullah Ibrahim, Rabha Waell Kumar, Govindaraj Hemantha Pal, Umapada Lu, Tong QA75 Electronic computers. Computer science One of the major causes of poor results in license plate recognition is low quality of images affected by multiple factors, such as severe illumination condition, complex background, different weather conditions, night light, and perspective distortions. In this paper, we propose a new mathematical model based on Riesz fractional operator for enhancing details of edge information in license plate images to improve the performances of text detection and recognition methods. The proposed model performs convolution operation of the Riesz fractional derivative over each input image by enhancing the edge strength in it. To test the performance of the proposed model, we conduct experiments on benchmark license plate image databases, namely, UCSD and ICDAR 2015-SR competition text image databases. Experimental results on enhancement show that the proposed model outperforms the existing baseline enhancement techniques in terms of quality measures. Furthermore, experimental results on text detection and recognition show that text detection and recognition rates are improved significantly after enhancement compared with before enhancement. Institute of Electrical and Electronics Engineers 2018 Article PeerReviewed Raghunandan, K.S. and Shivakumara, Palaiahnakote and Jalab, Hamid Abdullah and Ibrahim, Rabha Waell and Kumar, Govindaraj Hemantha and Pal, Umapada and Lu, Tong (2018) Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 28 (9). pp. 2276-2288. ISSN 1051-8215, DOI https://doi.org/10.1109/TCSVT.2017.2713806 <https://doi.org/10.1109/TCSVT.2017.2713806>. https://doi.org/10.1109/TCSVT.2017.2713806 doi:10.1109/TCSVT.2017.2713806 |
| spellingShingle | QA75 Electronic computers. Computer science Raghunandan, K.S. Shivakumara, Palaiahnakote Jalab, Hamid Abdullah Ibrahim, Rabha Waell Kumar, Govindaraj Hemantha Pal, Umapada Lu, Tong Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition |
| title | Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition |
| title_full | Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition |
| title_fullStr | Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition |
| title_full_unstemmed | Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition |
| title_short | Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition |
| title_sort | riesz fractional based model for enhancing license plate detection and recognition |
| topic | QA75 Electronic computers. Computer science |
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