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Image-based sequence recognition has been a long-standing research topic in computer vision.
Some approaches to best-match file searching
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Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Y. Simard, and P. Frasconi · 1994
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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Gradient-based learning applied to document recognition
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Learning precise timing with LSTM recurrent networks
F. A. Gers, N. N. Schraudolph, and J. Schmidhuber · 2002
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ICDAR 2003 robust reading competitions: entries, results, and future directions
S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, R. Young, K. Ashida, H. Nagai, M. Okamoto, H. Yamamoto, H. Miyao, J. Zhu, W. Ou, C. Wolf, J. Jolion, L. Todoran, M. Worring, and X. Lin · 2005
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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
A. Graves, S. Fernández, F. J. Gomez, and J. Schmidhuber · 2006
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A novel connectionist system for unconstrained handwriting recognition
A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber · 2009
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Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
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End-to-end scene text recognition
K. Wang, B. Babenko, and S. Belongie · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Scene text recognition using higher order language priors
A. Mishra, K. Alahari, and C. V. Jawahar · 2012
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Optical music recognition: state-of-the-art and open issues
A. Rebelo, I. Fujinaga, F. Paszkiewicz, A. R. S. Marçal, C. Guedes, and J. S. Cardoso · 2012
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End-to-end text recognition with convolutional neural networks
T. Wang, D. J. Wu, A. Coates, and A. Y. Ng · 2012
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ADADELTA: an adaptive learning rate method
M. D. Zeiler · 2012
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Photoocr: Reading text in uncontrolled conditions
A. Bissacco, M. Cummins, Y. Netzer, and H. Neven · 2013
Rich feature hierarchies for accurate object detection and semantic segmentation
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Synthetic data and artificial neural networks for natural scene text recognition
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Deep features for text spotting
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Accurate scene text recognition based on recurrent neural network
B. Su and S. Lu · 2014
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Strokelets: A learned multi-scale representation for scene text recognition
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Whole is greater than sum of parts: Recognizing scene text words
V. Goel, A. Mishra, K. Alahari, and C. V. Jawahar · 2013
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Speech recognition with deep recurrent neural networks
A. Graves, A. Mohamed, and G. E. Hinton · 2013
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ICDAR 2013 robust reading competition
D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. Almazán, and L. de las Heras · 2013
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Word spotting and recognition with embedded attributes
J. Almazán, A. Gordo, A. Fornés, and E. Valveny · 2014
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End-to-end text recognition with hybrid HMM maxout models
O. Alsharif and J. Pineau · 2014
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C. Yao, X. Bai, B. Shi, and W. Liu · 2014
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Supervised mid-level features for word image representation
A. Gordo · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep structured output learning for unconstrained text recognition
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Reading text in the wild with convolutional neural networks
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Label embedding: A frugal baseline for text recognition
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