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Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
Workshop on Deep Learning, NIPS, 2014
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In this work we present a framework for the recognition of natural scene text.
Our framework does not require any human-labelled data, and performs word
recognition on the whole image holistically, departing from the character based
recognition systems of the past. The deep neural network models at the centre of
this framework are trained solely on data produced by a synthetic text generation
engine – synthetic data that is highly realistic and sufficient to replace real data,
giving us infinite amounts of training data. This excess of data exposes new possibilities
for word recognition models, and here we consider three models, each one
“reading” words in a different way: via 90k-way dictionary encoding, character
sequence encoding, and bag-of-N-grams encoding. In the scenarios of language
based and completely unconstrained text recognition we greatly improve upon
state-of-the-art performance on standard datasets, using our fast, simple machinery
and requiring zero data-acquisition costs.
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BibTex reference:
@InProceedings{Jaderberg14c,
author = "Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman",
title = "Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition",
booktitle = "Workshop on Deep Learning, NIPS",
year = "2014",
}