License plate recognition using deep learning

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Rakhshani Sajed; Rashedi Esmat; NEZAMABADI POUR HOSSEIN

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Journal: MACHINE VISION AND IMAGE PROCESSING Year:2019 | Volume:6 | Issue:1 Page(s): 31-46

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Information Journal Paper

Title

License plate recognition using deep learning

Author(s)

Rakhshani Sajed | Rashedi Esmat | NEZAMABADI POUR HOSSEIN | Issue Writer Certificate

Keywords

Autoencoder

Deep Learning

Encoder-decoder neural network

Vehicle license plate recognition

Abstract

In this paper, a method based on deep learning is presented to highlight and recognize the Iranian license plate numbers. The current research uses the convolutional neural network with the encoder-decoder structure to enhance the image and highlight the plate image numbers instead of using traditional image enhancement techniques. The proposed network can highlight vehicle license plate numbers by learning the plate images in various conditions. After that, the plate numbers are recognized from the reproduced image using a recurrent neural network without the need to plate image segmentation. This method can reduce the error caused by the license plate number segmentation. The proposed method reached the final recognition rate up to 94٫ 19 percent on a database with 4000 test images for recognizing the license plates which is acceptable in comparison to three recent methods.

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APA: Copy

Rakhshani, Sajed, Rashedi, Esmat, & NEZAMABADI POUR, HOSSEIN. (2019). License plate recognition using deep learning. MACHINE VISION AND IMAGE PROCESSING, 6(1 ), 31-46. SID. https://sid.ir/paper/360082/en

Vancouver: Copy

Rakhshani Sajed, Rashedi Esmat, NEZAMABADI POUR HOSSEIN. License plate recognition using deep learning. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2019;6(1 ):31-46. Available from: https://sid.ir/paper/360082/en

IEEE: Copy

Sajed Rakhshani, Esmat Rashedi, and HOSSEIN NEZAMABADI POUR, “License plate recognition using deep learning,” MACHINE VISION AND IMAGE PROCESSING, vol. 6, no. 1 , pp. 31–46, 2019, [Online]. Available: https://sid.ir/paper/360082/en

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