# LPRNet: License Plate Recognition via Deep Neural Networks

arXiv: https://arxiv.org/abs/1806.10447 · HTML: https://mlsanity.com/paper/1806.10447/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2018.

## Built on by (most cited first, 13 of 13)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Indian Licence Plate Dataset in the wild](https://mlsanity.com/paper/2111.06054/) (2021)
- [A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot](https://mlsanity.com/paper/2408.05729/) (2024)
- [Video-based Vehicle Surveillance in the Wild: License Plate, Make, and Model Recognition with Self Reflective Vision-Language Models](https://mlsanity.com/paper/2508.01387/) (2025)
- [Image-free multi-character recognition](https://mlsanity.com/paper/2112.10587/) (2021)
- [FANVID: A Benchmark for Face and License Plate Recognition in Low-Resolution Videos](https://mlsanity.com/paper/2506.07304/) (2025)
- [LPTR-AFLNet: Lightweight Integrated Chinese License Plate Rectification and Recognition Network](https://mlsanity.com/paper/2507.16362/) (2025)
- [AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime](https://mlsanity.com/paper/2604.14661/) (2026)
- [Introducing One Sided Margin Loss for Solving Classification Problems in Deep Networks](https://mlsanity.com/paper/2206.01002/) (2022)
- [Semi-supervised Ranking for Object Image Blur Assessment](https://mlsanity.com/paper/2207.06085/) (2022)
- [TransLPRNet: Lite Vision-Language Network for Single/Dual-line Chinese License Plate Recognition](https://mlsanity.com/paper/2507.17335/) (2025)
- [LP-LLM: End-to-End Real-World Degraded License Plate Text Recognition via Large Multimodal Models](https://mlsanity.com/paper/2601.09116/) (2026)
- [Low-Power License Plate Detection and Recognition on a RISC-V Multi-Core MCU-Based Vision System](https://mlsanity.com/paper/2607.09768/) (2026)

## Abstract (arXiv)

This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.