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2005
ACM

Boosted decision trees for word recognition in handwritten document retrieval

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Boosted decision trees for word recognition in handwritten document retrieval
Recognition and retrieval of historical handwritten material is an unsolved problem. We propose a novel approach to recognizing and retrieving handwritten manuscripts, based upon word image classification as a key step. Decision trees with normalized pixels as features form the basis of a highly accurate AdaBoost classifier, trained on a corpus of word images that have been resized and sampled at a pyramid of resolutions. To stem problems from the highly skewed distribution of class frequencies, word classes with very few training samples are augmented with stochastically altered versions of the originals. This increases recognition performance substantially. On a standard corpus of 20 pages of handwritten material from the George Washington collection the recognition performance shows a substantial improvement in performance over previous published results (75% vs 65%). Following word recognition, retrieval is done using a language model over the recognized words. Retrieval perform...
Nicholas R. Howe, Toni M. Rath, R. Manmatha
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where SIGIR
Authors Nicholas R. Howe, Toni M. Rath, R. Manmatha
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