  In this paper we propose an approach to lexicon-free recognition of text in
scene images. Our approach relies on a LSTM-based soft visual attention model
learned from convolutional features. A set of feature vectors are derived from
an intermediate convolutional layer corresponding to different areas of the
image. This permits encoding of spatial information into the image
representation. In this way, the framework is able to learn how to selectively
focus on different parts of the image. At every time step the recognizer emits
one character using a weighted combination of the convolutional feature vectors
according to the learned attention model. Training can be done end-to-end using
only word level annotations. In addition, we show that modifying the beam
search algorithm by integrating an explicit language model leads to
significantly better recognition results. We validate the performance of our
approach on standard SVT and ICDAR'03 scene text datasets, showing
state-of-the-art performance in unconstrained text recognition.
