License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

Afshin Dehghan; Enrique G. Ortiz; Guang Shu; Syed Zain Masood

License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

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arxiv 1703.07330 v2 pith:LL2AG5SH submitted 2017-03-21 cs.CV

License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

Syed Zain Masood , Guang Shu , Afshin Dehghan , Enrique G. Ortiz This is my paper

classification cs.CV

keywords licenseplatesystemdetectionrecognitioncloudcnnsconvolutional

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This work details Sighthounds fully automated license plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neural Networks (CNNs) interlaced with accurate and efficient algorithms. The CNNs are trained and fine-tuned so that they are robust under different conditions (e.g. variations in pose, lighting, occlusion, etc.) and can work across a variety of license plate templates (e.g. sizes, backgrounds, fonts, etc). For quantitative analysis, we show that our system outperforms the leading license plate detection and recognition technology i.e. ALPR on several benchmarks. Our system is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud

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