# How many labeled license plates are needed?

arXiv: https://arxiv.org/abs/1808.08410 · HTML: https://mlsanity.com/paper/1808.08410/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition](https://mlsanity.com/paper/2108.06166/) (2021)
- [Tracking Based Semi-Automatic Annotation for Scene Text Videos](https://mlsanity.com/paper/2103.15488/) (2021)
- [Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging](https://mlsanity.com/paper/2209.05911/) (2022)

## Abstract (arXiv)

Training a good deep learning model often requires a lot of annotated data. As a large amount of labeled data is typically difficult to collect and even more difficult to annotate, data augmentation and data generation are widely used in the process of training deep neural networks. However, there is no clear common understanding on how much labeled data is needed to get satisfactory performance. In this paper, we try to address such a question using vehicle license plate character recognition as an example application. We apply computer graphic scripts and Generative Adversarial Networks to generate and augment a large number of annotated, synthesized license plate images with realistic colors, fonts, and character composition from a small number of real, manually labeled license plate images. Generated and augmented data are mixed and used as training data for the license plate recognition network modified from DenseNet. The experimental results show that the model trained from the generated mixed training data has good generalization ability, and the proposed approach achieves a new state-of-the-art accuracy on Dataset-1 and AOLP, even with a very limited number of original real license plates. In addition, the accuracy improvement caused by data generation becomes more significant when the number of labeled images is reduced. Data augmentation also plays a more significant role when the number of labeled images is increased.