Paper Info
Title
Attention and Language Ensemble for Scene Text Recognition with Convolutional Sequence Modeling.
Abstract
Recent dominant approaches for scene text recognition are mainly based on convolutional neural network (CNN) and recurrent neural network (RNN), where the CNN processes images and the RNN generates character sequences. Different from these methods, we propose an attention-based architecture1 which is completely based on CNNs. The distinctive characteristics of our method include: (1) the method follows encoder-decoder architecture, in which the encoder is a two-dimensional residual CNN and the decoder is a deep one-dimensional CNN. (2) An attention module that captures visual cues, and a language module that models linguistic rules are designed equally in the decoder. Therefore the attention and language can be viewed as an ensemble to boost predictions jointly. (3) Instead of using a single loss from language aspect, multiple losses from attention and language are accumulated for training the networks in an end-to-end way. We conduct experiments on standard datasets for scene text recognition, including Street View Text, IIIT5K and ICDAR datasets. The experimental results show our CNN-based method has achieved state-of-the-art performance on several benchmark datasets, even without the use of RNN.
Year
DOI
Venue
2018
10.1145/3240508.3240571
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018
Keywords
Field
DocType
Text recognition, convolutional neural networks, multi-level supervised information, attention model
Sensory cue,Computer vision,Language module,Residual,Convolutional neural network,Computer science,Recurrent neural network,Speech recognition,Sequence modeling,Encoder,Artificial intelligence,Text recognition
Conference
ISBN
Citations 
PageRank 
978-1-4503-5665-7
6
0.46
References 
Authors
29
6
Name
Order
Citations
PageRank
Shancheng Fang1254.43
Hongtao Xie243947.79
Zheng-Jun Zha32822152.79
Nannan Sun4221.57
Jianlong Tan5187.76
Yongdong Zhang62544166.91