TY - GEN
T1 - Attention enhanced ConvNet-RNN for Chinese vehicle license plate recognition
AU - Duan, Shiming
AU - Hu, Wei
AU - Li, Ruirui
AU - Li, Wei
AU - Sun, Shihao
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - As an important part of intelligent transportation system, vehicle license plate recognition requires high accuracy in an open environment. While a lot of approaches have been proposed, and achieved good performance to some extent, these approaches still have problems, for example, in the condition of characters’ distortion or partial occlusion. Segmentation-free VLPR systems compute the label in one pass using Long Short-Term Memory Network (LSTM), without individual segmentation step, their results tend to be not influenced by the segmentation accuracy. Based on the idea of Segmentation-free VLPR, this paper proposed an attention enhanced ConvNet-RNN (AC-RNN) for accurate Chinese Vehicle License Plate Recognition. The attention mechanism helps to locate the important instances in the step of recognition. While the ConvNet is used to extract features, the recurrent neural networks (RNN) with connectionist temporal classification (CTC) are applied for sequence labeling. The proposed AC-RNN was trained on a large generated dataset which contains various types of license plates in China. The AC-RNN could figure out the vehicle license even in cases of light changing, spatial distortion and partial blurry. Experiments showed that the AC-RNN performs better on the testing real images, increasing about 5% on accuracy, compared with classic ConvNet-RNN [8].
AB - As an important part of intelligent transportation system, vehicle license plate recognition requires high accuracy in an open environment. While a lot of approaches have been proposed, and achieved good performance to some extent, these approaches still have problems, for example, in the condition of characters’ distortion or partial occlusion. Segmentation-free VLPR systems compute the label in one pass using Long Short-Term Memory Network (LSTM), without individual segmentation step, their results tend to be not influenced by the segmentation accuracy. Based on the idea of Segmentation-free VLPR, this paper proposed an attention enhanced ConvNet-RNN (AC-RNN) for accurate Chinese Vehicle License Plate Recognition. The attention mechanism helps to locate the important instances in the step of recognition. While the ConvNet is used to extract features, the recurrent neural networks (RNN) with connectionist temporal classification (CTC) are applied for sequence labeling. The proposed AC-RNN was trained on a large generated dataset which contains various types of license plates in China. The AC-RNN could figure out the vehicle license even in cases of light changing, spatial distortion and partial blurry. Experiments showed that the AC-RNN performs better on the testing real images, increasing about 5% on accuracy, compared with classic ConvNet-RNN [8].
KW - Attention
KW - Long short-term memory network
KW - Recurrent neural networks
KW - Vehicle license plate recognition
UR - http://www.scopus.com/inward/record.url?scp=85057143551&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-03335-4_36
DO - 10.1007/978-3-030-03335-4_36
M3 - Conference contribution
AN - SCOPUS:85057143551
SN - 9783030033347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 417
EP - 428
BT - Pattern Recognition and Computer Vision - First Chinese Conference, PRCV 2018, Proceedings
A2 - Liu, Cheng-Lin
A2 - Tan, Tieniu
A2 - Zhou, Jie
A2 - Lai, Jian-Huang
A2 - Chen, Xilin
A2 - Zheng, Nanning
A2 - Zha, Hongbin
PB - Springer Verlag
T2 - 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018
Y2 - 23 November 2018 through 26 November 2018
ER -