License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks
arXiv:1703.07330
Abstract
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
10 pages, 4 figures, 2 tables
References in corpus (4)
- Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
- DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
- Segmentation-free Vehicle License Plate Recognition using ConvNet-RNN
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- Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
- ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene
- SuperOCR: A Conversion from Optical Character Recognition to Image Captioning
- Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks
- A Robust Attentional Framework for License Plate Recognition in the Wild
- Construction of efficient detectors for character information recognition