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Handwriting Recognition with Large Multidimensional Long Short-Term Memory Recurrent Neural Networks.

, , and . ICFHR, page 228-233. IEEE Computer Society, (2016)

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Improvement of Context Dependent Modeling for Arabic Handwriting Recognition., , and . ICFHR, page 494-499. IEEE Computer Society, (2014)On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting Recognition., , and . ICFHR, page 193-198. IEEE Computer Society, (2016)Sequence-discriminative training of recurrent neural networks., , , , and . ICASSP, page 2100-2104. IEEE, (2015)Open-Lexicon Language Modeling Combining Word and Character Levels., , , , and . ICFHR, page 343-348. IEEE Computer Society, (2014)Comparison of Bernoulli and Gaussian HMMs Using a Vertical Repositioning Technique for Off-Line Handwriting Recognition., , , , , and . ICFHR, page 3-7. IEEE Computer Society, (2012)Cross-entropy vs. squared error training: a theoretical and experimental comparison., , and . INTERSPEECH, page 1756-1760. ISCA, (2013)MLLP-UPV and RWTH Aachen Spanish ASR Systems for the IberSpeech-RTVE 2018 Speech-to-Text Transcription Challenge., , , , , , , , , and . IberSPEECH, page 257-261. ISCA, (2018)Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge., , , , , , , , , and . KDD Cup, volume 7 of JMLR Proceedings, page 77-88. JMLR.org, (2009)A comprehensive study of batch construction strategies for recurrent neural networks in MXNet., , and . CoRR, (2017)Multilingual Off-Line Handwriting Recognition in Real-World Images., , , and . Document Analysis Systems, page 121-125. IEEE Computer Society, (2014)