End-to-End Text Recognition with Convolutional Neural Networks

Tao Wang, David J. Wu, Adam Coates, and Andrew Y. Ng

International Conference on Pattern Recognition (ICPR), 2012



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.

  author    = {Tao Wang and David J. Wu and Adam Coates and Andrew Y. Ng},
  title     = {End-to-end Text Recognition with Convolutional Neural Networks},
  booktitle = {International Conference on Pattern Recognition ({ICPR})},
  year      = {2012}