We present an Automatic License Plate Recognition system designed around Convolutional Neural Networks (CNNs) and trained over synthetic plate images. We first design CNNs suitable for plate and character detection, sharing a common architecture and training procedure. Then, we generate synthetic images that account for the varying illumination and pose conditions encountered with real plate images and we use exclusively such synthetic images to train our CNNs. Experiments with real vehicle images captured in natural light with commodity imaging systems show precision and recall in excess of 93% despite our networks are trained exclusively on synthetic images.

Automatic License Plate Recognition with Convolutional Neural Networks Trained on Synthetic Data / Bjorklund, TOMAS PER ROLF; Fiandrotti, Attilio; Mauro, Annarumma; Gianluca, Francini; Magli, Enrico. - ELETTRONICO. - Proceding of Multimedia Signal Processing (MMSP), 2017 IEEE 19th International Workshop on:(2017), pp. 1-6. ((Intervento presentato al convegno IEEE 19th International Workshop on Multimedia Signal Processing (MMSP 2017) tenutosi a Luton, UK nel 16-18 October 2017 [10.1109/MMSP.2017.8122260].

Automatic License Plate Recognition with Convolutional Neural Networks Trained on Synthetic Data

BJORKLUND, TOMAS PER ROLF;FIANDROTTI, ATTILIO;MAGLI, ENRICO
2017

Abstract

We present an Automatic License Plate Recognition system designed around Convolutional Neural Networks (CNNs) and trained over synthetic plate images. We first design CNNs suitable for plate and character detection, sharing a common architecture and training procedure. Then, we generate synthetic images that account for the varying illumination and pose conditions encountered with real plate images and we use exclusively such synthetic images to train our CNNs. Experiments with real vehicle images captured in natural light with commodity imaging systems show precision and recall in excess of 93% despite our networks are trained exclusively on synthetic images.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2687096
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