PixMap: automatic license plate recognition with convolutional neural network based on saliency maps

Assan Sanogo; Arcadi Llanza; Nadiya Shvai; Abul Hasnat; Marouan Khata; Yassine El Khattabi; Antoine Meicler; Alice Hemery; Amir Nakib

doi:10.1117/12.2526064

21 June 2019 PixMap: automatic license plate recognition with convolutional neural network based on saliency maps

Assan Sanogo, Arcadi Llanza, Nadiya Shvai, Abul Hasnat, Marouan Khata, Yassine El Khattabi, Antoine Meicler, Alice Hemery, Amir Nakib

Author Affiliations +

Proceedings Volume 11062, Digital Optical Technologies 2019; 110620O (2019) https://doi.org/10.1117/12.2526064
Event: SPIE Digital Optical Technologies, 2019, Munich, Germany

Abstract

Automatic License Plate Recognition (ALPR) is a technology designed to automatically read vehicle license plates. Traditional ALPR systems first detect the License Plate (LP), then apply the Optical Character Recognition (OCR) pipeline, which includes LP image pre-processing, character segmentation, character classification and post-processing. An ALPR system developed with such approaches often fails to provide acceptable results due to numerous challenging situations, which significantly increase the appearance variability of LPs as well as the characters to be classified. Recently, Convolutional Neural Network (CNN) models have proved efficient for ALPR problems. However, many of these CNN-based methods yet exhibit vulnerabilities to properly localize the region of the characters’ sequence and therefore provide an incorrect segmentation. Herein, this paper presents a novel real-time ALPR system that uses the concept of saliency map within the CNN model. The key contribution is at the segmentation step where the characters are located by means of the saliency map, which helps to refine the character classification step. The proposed ALPR pipeline consists of the two modules: 1) LPlocalization-CNN to detect the LP and 2) Saliency-Map-CNN to segment the characters in the LP. Experiments are conducted on a private and two public datasets and the proposed method is compared to the state-of-the-art methods. Results show that it performs well with respect to both accuracy and computation time, and hence clearly demonstrate the usefulness of the proposed system for the real-world ALPR applications.

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Assan Sanogo, Arcadi Llanza, Nadiya Shvai, Abul Hasnat, Marouan Khata, Yassine El Khattabi, Antoine Meicler, Alice Hemery, and Amir Nakib "PixMap: automatic license plate recognition with convolutional neural network based on saliency maps", Proc. SPIE 11062, Digital Optical Technologies 2019, 110620O (21 June 2019); https://doi.org/10.1117/12.2526064

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