Self-training for Handwritten Text Line Recognition
Off-line handwriting recognition deals with the task of automatically recognizing handwritten text from images, for example from scanned sheets of paper. Due to the tremendous variations of writing styles encountered between different individuals, this is a very challenging task. Traditionally, a recognition system is trained by using a large corpus of handwritten text that has to be transcribed manually. This, however, is a laborious and costly process. Recent developments have proposed semi-supervised learning, which reduces the need for manually transcribed text by adding large amounts of handwritten text without transcription to the training set. The current paper is the first one, to the knowledge of the authors, where semi-supervised learning for unconstrained handwritten text line recognition is proposed. We demonstrate the applicability of self-training, a form of semi-supervised learning, to neural network based handwriting recognition. Through a set of experiments we show that text without transcription can successfully be used to significantly increase the performance of a handwriting recognition system.
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