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Greedy search for active learning of OCR
A. Agarwal, R. Garg,
Published in
Pages: 837 - 841
Active learning and crowd sourcing are becoming increasingly popular in the machine learning community for fast and cost effective generation of labels for large volumes of data. However, such labels may be noisy. So, it becomes important to ignore the noisy labels for building of a good classifier. We propose a framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples. The approach inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance. © 2013 IEEE.
About the journal
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR