  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.
