# Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs

arXiv: https://arxiv.org/abs/1601.05610 · HTML: https://mlsanity.com/paper/1601.05610/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2016.

## Built on by (most cited first, 9 of 9)

- [Trends in Vehicle Re-identification Past, Present, and Future: A Comprehensive Review](https://mlsanity.com/paper/2102.09744/) (2021)
- [Deep Learning Based Framework for Iranian License Plate Detection and Recognition](https://mlsanity.com/paper/2201.06825/) (2022)
- [Vehicle and License Plate Recognition with Novel Dataset for Toll Collection](https://mlsanity.com/paper/2202.05631/) (2022)
- [Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset](https://mlsanity.com/paper/2206.00859/) (2022)
- [End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks](https://mlsanity.com/paper/2202.10277/) (2022)
- [TextMatcher: Cross-Attentional Neural Network to Compare Image and Text](https://mlsanity.com/paper/2205.05507/) (2022)
- [Global License Plate Dataset](https://mlsanity.com/paper/2405.10949/) (2024)
- [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)

In this work, we tackle the problem of car license plate detection and recognition in natural scene images. Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition. Firstly, we train a 37-class convolutional neural network (CNN) to detect all characters in an image, which results in a high recall, compared with conventional approaches such as training a binary text/non-text classifier. False positives are then eliminated by the second plate/non-plate CNN classifier. Bounding box refinement is then carried out based on the edge information of the license plates, in order to improve the intersection-over-union (IoU) ratio. The proposed cascade framework extracts license plates effectively with both high recall and precision. Last, we propose to recognize the license characters as a sequence labelling problem. A recurrent neural network (RNN) with long short-term memory (LSTM) is trained to recognize the sequential features extracted from the whole license plate via CNNs. The main advantage of this approach is that it is segmentation free. By exploring context information and avoiding errors caused by segmentation, the RNN method performs better than a baseline method of combining segmentation and deep CNN classification; and achieves state-of-the-art recognition accuracy.