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Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary(More)
—The automatic transcription of historical documents is vital for the creation of digital libraries. In order to make images of valuable old documents amenable to browsing, a transcription of high accuracy is needed. In this paper, two state-of-the art recognizers originally developed for modern scripts are applied to medieval documents. The first is based(More)
For retrieving keywords from scanned handwritten documents, we present a word spotting system that is based on character Hidden Markov Models. In an efficient lexicon-free approach, arbitrary keywords can be spotted without pre-segmenting text lines into words. For a multi-writer scenario on the IAM off-line database as well as for two single writer(More)
—Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bidirectional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online handwritten documents with non-text content. It(More)
BACKGROUND Low-molecular-weight heparins (LMWH) have been shown to be safer, more effective and more convenient than unfractionated heparin (UFH) in many clinical situations. However, their use is limited in patients with renal insufficiency (RI) due to bioaccumulation. METHOD The literature is critically reviewed and known pharmacokinetic properties are(More)
—Handwritten word spotting aims at making document images amenable to browsing and searching by keyword retrieval. In this paper, we present a word spotting system based on Hidden Markov Models (HMM) that uses trained subword models to spot keywords. With the proposed method, arbitrary keywords can be spotted that do not need to be present in the training(More)
—Segmenting page images into text lines is a crucial pre-processing step for automated reading of historical documents. Challenging issues in this open research field are given e.g. by paper or parchment background noise, ink bleed-through, artifacts due to aging, stains, and touching text lines. In this paper, we present a novel binarization-free line(More)
Handwriting recognition in historical documents is vital for the creation of digital libraries. The creation of readily available ground truth data plays a central role for the development of new recognition technologies. For historical documents, ground truth creation is more difficult and time-consuming when compared with modern documents. In this paper,(More)