How many labeled license plates are needed?
Changhao Wu, Shugong Xu, Guocong Song, Shunqing Zhang

TL;DR
This paper investigates the minimum amount of labeled license plate data needed for effective recognition, demonstrating that data augmentation and generation significantly improve accuracy with limited real data.
Contribution
It introduces a method combining computer graphics and GANs to generate realistic license plate images, reducing the need for large labeled datasets.
Findings
Generated data improves model generalization.
Achieves state-of-the-art accuracy with limited real data.
Data augmentation's impact increases as real data decreases.
Abstract
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…
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Taxonomy
TopicsVehicle License Plate Recognition · Handwritten Text Recognition Techniques · Advanced Neural Network Applications
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Batch Normalization · Convolution · Average Pooling · Concatenated Skip Connection · Global Average Pooling · Dense Block · Kaiming Initialization · 1x1 Convolution · Dropout
