Application of Convolutional Neural Networks for Automatic Number Plate Recognition on Complex Background Images; Applied Mechanics and Materials; Vol. 756 : Mechanical Engineering, Automation and Control Systems (MEACS2014)
| Parent link: | Applied Mechanics and Materials: Scientific Journal Vol. 756 : Mechanical Engineering, Automation and Control Systems (MEACS2014).— 2015.— [P. 695-703] |
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| Main Author: | |
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| Other Authors: | , |
| Summary: | Title screen The relevance of this study is stipulated by the necessity of designing techniques, algorithms, and programs improving the efficiency of automatic number plate recognition (ANPR) on images with complex backgrounds.Purpose: The aim of this work is to improve the efficiency of automatic number plate recognition on images with complex backgrounds using methods, algorithms, and programs invariant to affine and projective transformations.Design/methodology: Such techniques as artificial intelligence, pattern identification and recognition, the theory of artificial neural networks (ANN), convolutional neural networks (CNN), evolutionary algorithms, mathematical modeling, the probability theory and mathematical statistics were applied via Visual Studio and MatLab software.Findings: The software is developed allowing the automatic number plate recognition on complex background images. The convolutional neural network comprising seven layers is suggested to identify the plate localization, i.e. finding and isolating the plate on the picture. The pixel intensity histogram-based algorithm was used for character segmentation or finding individual characters on the plates. The convolutional neural network comprising six layers is designed to recognize characters. The suggested software system allows automatic number plate recognition at large angles of inclinations and rather a high speed. Режим доступа: по договору с организацией-держателем ресурса |
| Language: | English |
| Published: |
2015
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| Series: | Image and Signal Processing, Recognition, Information Processing and Applied Technologies |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4028/www.scientific.net/AMM.756.695 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=641421 |
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| 200 | 1 | |a Application of Convolutional Neural Networks for Automatic Number Plate Recognition on Complex Background Images |f A. A. Druki, Yu. A. Bolotova, V. G. Spitsyn | |
| 203 | |a Text |c electronic | ||
| 225 | 1 | |a Image and Signal Processing, Recognition, Information Processing and Applied Technologies | |
| 300 | |a Title screen | ||
| 330 | |a The relevance of this study is stipulated by the necessity of designing techniques, algorithms, and programs improving the efficiency of automatic number plate recognition (ANPR) on images with complex backgrounds.Purpose: The aim of this work is to improve the efficiency of automatic number plate recognition on images with complex backgrounds using methods, algorithms, and programs invariant to affine and projective transformations.Design/methodology: Such techniques as artificial intelligence, pattern identification and recognition, the theory of artificial neural networks (ANN), convolutional neural networks (CNN), evolutionary algorithms, mathematical modeling, the probability theory and mathematical statistics were applied via Visual Studio and MatLab software.Findings: The software is developed allowing the automatic number plate recognition on complex background images. The convolutional neural network comprising seven layers is suggested to identify the plate localization, i.e. finding and isolating the plate on the picture. The pixel intensity histogram-based algorithm was used for character segmentation or finding individual characters on the plates. The convolutional neural network comprising six layers is designed to recognize characters. The suggested software system allows automatic number plate recognition at large angles of inclinations and rather a high speed. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | 0 | |0 (RuTPU)RU\TPU\network\5920 |t Applied Mechanics and Materials |o Scientific Journal | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\6028 |t Vol. 756 : Mechanical Engineering, Automation and Control Systems (MEACS2014) |o International Conference, 16‐18 October, 2014, Tomsk, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) |v [P. 695-703] |d 2015 | |
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| 700 | 1 | |a Druki |b A. A. |c specialist in the field of informatics and computer technology |c assistant of Tomsk Polytechnic University, engineer |f 1985- |g Aleksey Alekseevich |3 (RuTPU)RU\TPU\pers\34610 |9 17972 | |
| 701 | 1 | |a Bolotova |b Yu. A. |c Specialist in the field of informatics and computer technology |c Associate Professor of Tomsk Polytechnic University, Candidate of technical sciences |f 1986- |g Yuliya Aleksandrovna |3 (RuTPU)RU\TPU\pers\33458 |9 17139 | |
| 701 | 1 | |a Spitsyn |b V. G. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, Doctor of technical sciences |f 1948- |g Vladimir Grigorievich |3 (RuTPU)RU\TPU\pers\33492 |9 17160 | |
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