.png){kind=icon}
返回
Convolutional Attention Networks for Scene Text Recognition
.png){kind=icon}
PRE
AI
DOI：10.1145/3231737.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this article, we present Convoluitional Attention Networks (CAN) for unconstrained scene text recognition. Recent dominant approaches for scene text recognition are mainly based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), where the CNN encodes images and the RNN generates character sequences. Our CAN is different from these methods; our CAN is completely built on CNN and includes an attention mechanism. The distinctive characteristics of our method include (i) CAN follows encoder-decoder architecture, in which the encoder is a deep two-dimensional CNN and the decoder is a one-dimensional CNN; (ii) the attention mechanism is applied in every convolutional layer of the decoder, and we propose a novel spatial attention method using average pooling; and (iii) position embeddings are equipped in both a spatial encoder and a sequence decoder to give our networks a sense of location. We conduct experiments on standard datasets for scene text recognition, including Street View Text, IIIT5K, and ICDAR datasets. The experimental results validate the effectiveness of different components and show that our convolutional-based method achieves state-of-the-art or competitive performance over prior works, even without the use of RNN.
Keyword:
Text recognition
text detection
convolutional neural networks
multi-level supervised information
attention model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
2.0K
被引数:
5.4K
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏
查看更多内容