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Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports.
Learning long-term dependencies with gradient descent is difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal · 1997
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Markovian models for sequential data
Yoshua Bengio · 1999
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An hmm-based approach for off-line unconstrained handwritten word modeling and recognition
A El-Yacoubi, Michel Gilloux, Robert Sabourin, and Ching Y. Suen · 1999
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Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001
Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, Jürgen Schmidhuber, et al · 2001
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Using a statistical language model to improve the performance of an hmm-based cursive handwriting recognition system
U-V Marti and Horst Bunke · 2001
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The iam-database: an english sentence database for offline handwriting recognition
U-V Marti and Horst Bunke · 2002
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A survey on off-line cursive word recognition
Alessandro Vinciarelli · 2002
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Recognition of cursive roman handwriting: past, present and future
Horst Bunke · 2003
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Rimes evaluation campaign for handwritten mail processing
Emmanuel Augustin, Matthieu Carré, Emmanuèle Grosicki, J-M Brodin, Edouard Geoffrois, and Françoise Prêteux · 2006
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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber · 2006
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Offline handwriting recognition with multidimensional recurrent neural networks
Alex Graves and Jürgen Schmidhuber · 2009
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A novel connectionist system for unconstrained handwriting recognition
Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber · 2009
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Connectionist speech recognition: a hybrid approach , volume 247
Herve A Bourlard and Nelson Morgan · 2012
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Dropout improves recurrent neural networks for handwriting recognition
Vu Pham, Théodore Bluche, Christopher Kermorvant, and Jérôme Louradour · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Residual lstm: Design of a deep recurrent architecture for distant speech recognition
Jaeyoung Kim, Mostafa El-Khamy, and Jungwon Lee · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Are multidimensional recurrent layers really necessary for handwritten text recognition?
Joan Puigcerver · 2017
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An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition
Baoguang Shi, Xiang Bai, and Cong Yao · 2017
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