Self-training Strategies for Handwriting Word Recognition
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Ground truth creation for handwriting recognition in historical documents
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Feature representations for the recognition of 3D emblematic gestures
HBU'10 Proceedings of the First international conference on Human behavior understanding
Self-training for handwritten text line recognition
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
An unsupervised learning based LSTM model: a new architecture
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
Improving handwritten keyword spotting with self-training
Proceedings of the 2011 ACM Symposium on Applied Computing
Tandem decoding of children's speech for keyword detection in a child-robot interaction scenario
ACM Transactions on Speech and Language Processing (TSLP)
A novel word spotting algorithm using bidirectional long short-term memory neural networks
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Combining neural networks to improve performance of handwritten keyword spotting
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
A synthesised word approach to word retrieval in handwritten documents
Pattern Recognition
Arabic handwriting recognition using structural and syntactic pattern attributes
Pattern Recognition
Text recognition in videos using a recurrent connectionist approach
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Bidirectional language model for handwriting recognition
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Journal of Network and Computer Applications
Handwriting beautification using token means
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Handwriting recognition in historical documents using very large vocabularies
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Normalizing historical orthography for OCR historical documents using LSTM
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Can we build language-independent OCR using LSTM networks?
Proceedings of the 4th International Workshop on Multilingual OCR
An iterative multimodal framework for the transcription of handwritten historical documents
Pattern Recognition Letters
Improving on-line handwritten recognition in interactive machine translation
Pattern Recognition
Keyword spotting in unconstrained handwritten Chinese documents using contextual word model
Image and Vision Computing
Effective balancing error and user effort in interactive handwriting recognition
Pattern Recognition Letters
Neural network language models for off-line handwriting recognition
Pattern Recognition
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Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.