Handwriting recognition by using deep learning to extract meaningful features

Work partially supported by the Spanish MINECO and FEDER founds under project TIN2017-85854-C4-2-R.

[1]  Tara N. Sainath,et al.  Auto-encoder bottleneck features using deep belief networks , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[4]  Gerhard Rigoll,et al.  A hybrid SVM/HMM acoustic modeling approach to automatic speech recognition , 2004, INTERSPEECH.

[5]  Hermann Ney,et al.  Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks , 2016, 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR).

[6]  Hermann Ney,et al.  Hierarchical hybrid MLP/HMM or rather MLP features for a discriminatively trained Gaussian HMM: A comparison for offline handwriting recognition , 2011, 2011 18th IEEE International Conference on Image Processing.

[7]  Sargur N. Srihari,et al.  Off-Line Cursive Script Word Recognition , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Marcus Liwicki,et al.  A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks , 2007 .

[9]  Isabelle Guyon,et al.  On-line cursive script recognition using time-delay neural networks and hidden Markov models , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[11]  Horst Bunke,et al.  Hidden Markov model-based ensemble methods for offline handwritten text line recognition , 2008, Pattern Recognit..

[12]  Christian Viard-Gaudin,et al.  MS-TDNN with global discriminant trainings , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[13]  Hermann Ney,et al.  Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition , 2014, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[14]  Geoffrey Leech,et al.  The tagged LOB Corpus : user's manual , 1986 .

[15]  Chafic Mokbel,et al.  Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Juan Miguel Vilar,et al.  Efficient computation of confidence intervals forword error rates , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Hermann Ney,et al.  A Comparison of Sequence-Trained Deep Neural Networks and Recurrent Neural Networks Optical Modeling for Handwriting Recognition , 2014, SLSP.

[18]  Georg Heigold,et al.  Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition , 2011, International Journal on Document Analysis and Recognition (IJDAR).

[19]  Volkmar Frinken,et al.  Neural network language models for off-line handwriting recognition , 2014, Pattern Recognition.

[20]  Hermann Ney,et al.  Tandem HMM with convolutional neural network for handwritten word recognition , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  Alexander H. Waibel,et al.  Online handwriting recognition: the NPen++ recognizer , 2001, International Journal on Document Analysis and Recognition.

[22]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[23]  Lior Wolf,et al.  CNN-N-Gram for HandwritingWord Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Hermann Ney,et al.  CTC in the Context of Generalized Full-Sum HMM Training , 2017, INTERSPEECH.

[25]  Giovanni Soda,et al.  Exploiting the past and the future in protein secondary structure prediction , 1999, Bioinform..

[26]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[27]  Alessandro Vinciarelli,et al.  A survey on off-line Cursive Word Recognition , 2002, Pattern Recognit..

[28]  Salvador España Boquera,et al.  Insights on the Use of Convolutional Neural Networks for Document Image Binarization , 2015, IWANN.

[29]  Salvador España Boquera,et al.  Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Elliot Singer,et al.  A speech recognizer using radial basis function neural networks in an HMM framework , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[31]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[32]  Isabelle Guyon,et al.  On-line cursive script recognition using time-delay neural networks and hidden Markov models , 2005, Machine Vision and Applications.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[35]  Horst Bunke,et al.  The IAM-database: an English sentence database for offline handwriting recognition , 2002, International Journal on Document Analysis and Recognition.

[36]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[37]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[38]  Kenneth M. Sayre,et al.  Machine recognition of handwritten words: A project report , 1973, Pattern Recognit..

[39]  Théodore Bluche,et al.  Deep Neural Networks for Large Vocabulary Handwritten Text Recognition , 2015 .

[40]  Jürgen Schmidhuber,et al.  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.

[41]  Hervé Bourlard,et al.  Connectionist Speech Recognition: A Hybrid Approach , 1993 .

[42]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[43]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[44]  Jan Cernocký,et al.  Probabilistic and Bottle-Neck Features for LVCSR of Meetings , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[45]  Pascal Vincent,et al.  Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives , 2012, ArXiv.

[46]  Salvador España Boquera,et al.  A combined Convolutional Neural Network and Dynamic Programming approach for text line normalization , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[47]  Hermann Ney,et al.  Integrated Handwriting Recognition And Interpretation Using Finite-State Models , 2004, Int. J. Pattern Recognit. Artif. Intell..

[48]  Hermann Ney,et al.  Feature Extraction with Convolutional Neural Networks for Handwritten Word Recognition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[49]  Joan Puigcerver,et al.  Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition? , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[50]  Hermann Ney,et al.  Improvements in RWTH's System for Off-Line Handwriting Recognition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[51]  Gernot A. Fink,et al.  Markov models for offline handwriting recognition: a survey , 2009, International Journal on Document Analysis and Recognition (IJDAR).

[52]  Sebastiano Impedovo,et al.  More than twenty years of advancements on Frontiers in handwriting recognition , 2014, Pattern Recognit..

[53]  Horst Bunke,et al.  Recognition of cursive Roman handwriting: past, present and future , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[54]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[55]  Bernadette Dorizzi,et al.  Sentence recognition through hybrid neuro-Markovian modeling , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[56]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[57]  Franz Josef Och,et al.  Minimum Error Rate Training in Statistical Machine Translation , 2003, ACL.

[58]  Joan Pastor Pellicer Neural Networks for Document Image and Text Processing , 2017 .

[59]  Anthony J. Robinson,et al.  An Off-Line Cursive Handwriting Recognition System , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[60]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[61]  T. Munich,et al.  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.

[62]  Christopher Kermorvant,et al.  Dropout Improves Recurrent Neural Networks for Handwriting Recognition , 2013, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[63]  J. Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[64]  Alejandro Héctor Toselli,et al.  Multimodal interactive transcription of text images , 2010, Pattern Recognit..

[65]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[66]  Endong Wang,et al.  Intel Math Kernel Library , 2014 .