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arXiv · 1603.03101

Recursive Recurrent Nets with Attention Modeling for OCR in the Wild

Chen-Yu Lee·Simon Osindero
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Abstract

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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Chen-Yu Lee, Simon Osindero. 2016-03-09. Recursive Recurrent Nets with Attention Modeling for OCR in the Wild. https://arxiv.org/abs/1603.03101

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