Abstract
This paper describes a novel vehicle plate recognition algorithm based on text detection and improved neocognitron neural network, similar to [1] and based on Fukushima’s neocognitron. The proposed recognition algorithm allows us to improve the recognition speed and accuracy comparing to both traditional neocognitron and some state-of-art algorithms (multilayer perceptron, topological methods). It can be used as a solution for image classification and analysis tasks. As an example, the neocognitron can be utilized for symbols recognition [2]. We propose several modifications comparing to the Fukushima’s modification of the neocognitron: namely, layer dimensions adjustment, threshold function and connection Gaussian kernel parameters estimation. The patterns’ width and height are taken into account independently in order to improve the recognition of patterns of slightly different dimensions. The learning and recognition calculations are performed as FFT convolutions in order to overcome the complexity of the neocognitron output calculations. The algorithm was tested on low-resolution (360 ×288) video sequences and gave more accurate results comparing to the state-of-the-art methods for low-resolution test set.
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Kangin, D., Kolev, G., Angelov, P. (2013). Vehicle Plate Recognition Using Improved Neocognitron Neural Network. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_78
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DOI: https://doi.org/10.1007/978-3-642-40728-4_78
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