License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks
This provides a robust solution for automated license plate recognition, useful for applications like traffic monitoring and security, but it is incremental as it builds on existing deep learning methods.
The paper tackles automated license plate detection and recognition by using a sequence of deep Convolutional Neural Networks, achieving performance that outperforms the leading technology ALPR on several benchmarks.
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