The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Unconstrained on-line handwriting recognition is typically approached within the framework of generative HMM-based classifiers. In this paper, we introduce a novel discriminative method that relies, in contrast, on explicit grapheme segmentation and SVM-based character recognition. In addition to single character recognition with rejection, bi-characters are recognized in order to refine the recognition...
Being able to search for words or phrases in historic handwritten documents is of paramount importance when preserving cultural heritage. Storing scanned pages of written text can save the information from degradation, but it does not make the textual information readily available. Automatic keyword spotting systems for handwritten historic documents can fill this gap. However, most such systems have...
Automatic transcription of historical documents is vital for the creation of digital libraries. In this paper we propose graph similarity features as a novel descriptor for handwriting recognition in historical documents based on Hidden Markov Models. Using a structural graph-based representation of text images, a sequence of graph similarity features is extracted by means of dissimilarity embedding...
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 set. Also, no text line segmentation is required...
The automatic transcription of historical documents is vital for the creation of digital libraries.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 on Hidden Markov Models and the second uses...
Building recognition systems for historical documents is a difficult task. Especially, when it comes to medieval scripts. The complexity is mainly affected by the poor quality and the small quantity of the data available. In this paper we apply an HMM based recognition system to medieval manuscripts from the 13th century written in Middle High German. The recognition system, which was originally developed...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.