# License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1703.07330 · HTML: https://mlsanity.com/paper/1703.07330/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2017.

## Built on by (most cited first, 6 of 6)

- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot](https://mlsanity.com/paper/2408.05729/) (2024)
- [PatrolVision: Automated License Plate Recognition in the wild](https://mlsanity.com/paper/2504.10810/) (2025)
- [Layout-Independent License Plate Recognition via Integrated Vision and Language Models](https://mlsanity.com/paper/2510.10533/) (2025)
- [Street-Legal Physical-World Adversarial Rim for License Plates](https://mlsanity.com/paper/2604.02457/) (2026)

## Abstract (arXiv)

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