Reading Scene Text with Attention Convolutional Sequence Modeling

TL;DR

This paper introduces an end-to-end convolutional attention network for scene text recognition that is faster and more efficient than RNN-based methods, achieving state-of-the-art results on standard benchmarks.

Contribution

The paper proposes replacing RNNs with convolutional layers and residual attention modules for scene text recognition, improving speed and discriminability.

Findings

9 times faster than Bidirectional LSTM

Achieves state-of-the-art performance on benchmarks

Effective in suppressing background noise

Abstract

Reading text in the wild is a challenging task in the field of computer vision. Existing approaches mainly adopted Connectionist Temporal Classification (CTC) or Attention models based on Recurrent Neural Network (RNN), which is computationally expensive and hard to train. In this paper, we present an end-to-end Attention Convolutional Network for scene text recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers to effectively capture the contextual dependencies of the input sequence, which is characterized by lower computational complexity and easier parallel computation. Compared to the chain structure of recurrent networks, the Convolutional Neural Network (CNN) provides a natural way to capture long-term dependencies between elements, which is 9 times faster than Bidirectional Long Short-Term Memory (BLSTM). Furthermore, in order to enhance the representation of foreground text and suppress the background noise, we incorporate the residual attention modules into a small densely connected network to improve the discriminability of CNN features. We validate the performance of our approach on the standard benchmarks, including the Street View Text, IIIT5K and ICDAR datasets. As a result, state-of-the-art or highly-competitive performance and efficiency show the superiority of the proposed approach.