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Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template...
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...
For historical documents, available transcriptions typically are inaccurate when compared with the scanned document images. Not only the position of the words and sentences are unknown, but also the correct image transcription may not be matched exactly. An error-tolerant alignment is needed to make the document images amenable to browsing and searching in digital libraries. In this paper, we propose...
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