  Convolutional Recurrent Neural Networks (CRNNs) excel at scene text
recognition. Unfortunately, they are likely to suffer from vanishing/exploding
gradient problems when processing long text images, which are commonly found in
scanned documents. This poses a major challenge to goal of completely solving
Optical Character Recognition (OCR) problem. Inspired by recently proposed
memory-augmented neural networks (MANNs) for long-term sequential modeling, we
present a new architecture dubbed Convolutional Multi-way Associative Memory
(CMAM) to tackle the limitation of current CRNNs. By leveraging recent memory
accessing mechanisms in MANNs, our architecture demonstrates superior
performance against other CRNN counterparts in three real-world long text OCR
datasets.
