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Recursive Recurrent Nets With Attention Modeling For OCR In The Wild

Chen-Yu Lee, Simon Osindero . 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 – 458 citations

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3d Representation CVPR Datasets Evaluation Interdisciplinary Approaches Model Architecture Neural Machine Translation Tools Training Techniques Variational Autoencoders

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.

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