License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

TL;DR

This paper presents a deep learning-based system for automated license plate detection and recognition that outperforms existing methods across various conditions and templates, utilizing convolutional neural networks and efficient algorithms.

Contribution

The work introduces a robust CNN-based license plate recognition system that is trained for high accuracy under diverse real-world conditions and is accessible via a cloud API.

Findings

Outperforms leading ALPR technology on multiple benchmarks

Robust under variations in pose, lighting, and occlusion

Applicable across diverse license plate styles

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