Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Computer Assisted Transcription of Handwritten Text Images
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Semi-supervised Learning for Handwriting Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Multimodal interactive transcription of text images
Pattern Recognition
Semi-automatic training sets acquisition for handwriting recognition
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods
Lampung - a new handwritten character benchmark: database, labeling and recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
A Semi-supervised Ensemble Learning Approach for Character Labeling with Minimal Human Effort
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards Semi-supervised Transcription of Handwritten Historical Weather Reports
DAS '12 Proceedings of the 2012 10th IAPR International Workshop on Document Analysis Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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Training recognizers for handwritten characters is still a very time consuming task involving tremendous amounts of manual annotations by experts. In this paper we present semi-supervised labeling strategies that are able to considerably reduce the human effort. We propose two different methods to label and later recognize characters in collections of historical archive documents. The first one is based on clustering of different feature representations and the second one incorporates a simultaneous retrieval on different representations. Hence, both approaches are based on multi-view learning and later apply a voting procedure for reliably propagating annotations to unlabeled data. We evaluate our methods on the MNIST database of handwritten digits and introduce a realistic application in form of a database of handwritten historical weather reports. The experiments show that our method is able to significantly reduce the human effort that is required to build a character recognizer for the data collection considered while still achieving recognition rates that are close to a supervised classification experiment.