  We present recursive recurrent neural networks with attention modeling
(R$^2$AM) for lexicon-free optical character recognition in natural scene
images. The primary advantages of the proposed method are: (1) use of recursive
convolutional neural networks (CNNs), which allow for parametrically efficient
and effective image feature extraction; (2) an implicitly learned
character-level language model, embodied in a recurrent neural network which
avoids the need to use N-grams; and (3) the use of a soft-attention mechanism,
allowing the model to selectively exploit image features in a coordinated way,
and allowing for end-to-end training within a standard backpropagation
framework. We validate our method with state-of-the-art performance on
challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.
