End-to-end text recognition with convolutional neural networks
Proceedings of the 21st international conference on pattern …, 2012•ieeexplore.ieee.org
Full end-to-end text recognition in natural images is a challenging problem that has received
much attention recently. Traditional systems in this area have relied on elaborate models
incorporating carefully hand-engineered features or large amounts of prior knowledge. In
this paper, we take a different route and combine the representational power of large,
multilayer neural networks together with recent developments in unsupervised feature
learning, which allows us to use a common framework to train highly-accurate text detector …
much attention recently. Traditional systems in this area have relied on elaborate models
incorporating carefully hand-engineered features or large amounts of prior knowledge. In
this paper, we take a different route and combine the representational power of large,
multilayer neural networks together with recent developments in unsupervised feature
learning, which allows us to use a common framework to train highly-accurate text detector …
Full end-to-end text recognition in natural images is a challenging problem that has received much attention recently. Traditional systems in this area have relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this paper, we take a different route and combine the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows us to use a common framework to train highly-accurate text detector and character recognizer modules. Then, using only simple off-the-shelf methods, we integrate these two modules into a full end-to-end, lexicon-driven, scene text recognition system that achieves state-of-the-art performance on standard benchmarks, namely Street View Text and ICDAR 2003.
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