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ICDAR
2011
IEEE

Co-training for Handwritten Word Recognition

9 years 1 months ago
Co-training for Handwritten Word Recognition
—To cope with the tremendous variations of writing styles encountered between different individuals, unconstrained automatic handwriting recognition systems need to be trained on large sets of labeled data. Traditionally, the training data has to be labeled manually, which is a laborious and costly process. Semi-supervised learning techniques offer methods to utilize unlabeled data, which can be obtained cheaply in large amounts in order, to reduce the need for labeled data. In this paper, we propose the use of Co-Training for improving the recognition accuracy of two weakly trained handwriting recognition systems. The first one is based on Recurrent Neural Networks while the second one is based on Hidden Markov Models. On the IAM off-line handwriting database we demonstrate a significant increase of the recognition accuracy can be achieved with Co-Training for single word recognition. Keywords-Semi-supervised Learning, Co-Training, Handwriting Recognition, Single Word Recognition,...
Volkmar Frinken, Andreas Fischer, Horst Bunke, Ali
Added 24 Dec 2011
Updated 24 Dec 2011
Type Journal
Year 2011
Where ICDAR
Authors Volkmar Frinken, Andreas Fischer, Horst Bunke, Alicia Fornés
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