  Text recognition has attracted considerable research interests because of its
various applications. The cutting-edge text recognition methods are based on
attention mechanisms. However, most of attention methods usually suffer from
serious alignment problem due to its recurrency alignment operation, where the
alignment relies on historical decoding results. To remedy this issue, we
propose a decoupled attention network (DAN), which decouples the alignment
operation from using historical decoding results. DAN is an effective, flexible
and robust end-to-end text recognizer, which consists of three components: 1) a
feature encoder that extracts visual features from the input image; 2) a
convolutional alignment module that performs the alignment operation based on
visual features from the encoder; and 3) a decoupled text decoder that makes
final prediction by jointly using the feature map and attention maps.
Experimental results show that DAN achieves state-of-the-art performance on
multiple text recognition tasks, including offline handwritten text recognition
and regular/irregular scene text recognition.
