Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition

Abstract

Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5% over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2x faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.

References in corpus (16)

• Adam: A Method for Stochastic Optimization
• Distilling the Knowledge in a Neural Network
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Conditional Generative Adversarial Nets
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• ADADELTA: An Adaptive Learning Rate Method
• An overview of gradient descent optimization algorithms
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Recurrent Neural Network Regularization
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Improved Techniques for Training GANs
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
• Semi-Supervised Learning with Generative Adversarial Networks
• Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs
• Synthetic Data for Text Localisation in Natural Images

Cited by in corpus (7)

• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
• Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data
• DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
• Parametric Synthesis of Text on Stylized Backgrounds using PGGANs
• A Robust Attentional Framework for License Plate Recognition in the Wild
• How many labeled license plates are needed?