  Attention-based encoder-decoder framework is widely used in the scene text
recognition task. However, for the current state-of-the-art(SOTA) methods,
there is room for improvement in terms of the efficient usage of local visual
and global context information of the input text image, as well as the robust
correlation between the scene processing module(encoder) and the text
processing module(decoder). In this paper, we propose a Representation and
Correlation Enhanced Encoder-Decoder Framework(RCEED) to address these
deficiencies and break performance bottleneck. In the encoder module, local
visual feature, global context feature, and position information are aligned
and fused to generate a small-size comprehensive feature map. In the decoder
module, two methods are utilized to enhance the correlation between scene and
text feature space. 1) The decoder initialization is guided by the holistic
feature and global glimpse vector exported from the encoder. 2) The feature
enriched glimpse vector produced by the Multi-Head General Attention is used to
assist the RNN iteration and the character prediction at each time step.
Meanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's
generalization towards changeable texts. Extensive experiments on the
benchmarks demonstrate the advantageous performance of RCEED in scene text
recognition tasks, especially the irregular ones.
