Titlebook: Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text; Alejandro Héctor Toselli,Joan Puigce

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Empirical Validation of Probabilistic Indexing Methods,rs, require empirical validation. This is the main purpose of this chapter. In particular, the most relevant questions that we aim to answer trough the experiments are:.1. As compared with the HTR-oriented formulation proposed in Sec. 3.4, how do the various lexicon-based image-processing-oriented p

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Probabilistic Interpretation of Traditional KWS Approaches, are covered. In particular, we study schemes and techniques which have long been considered among the most successful. This includes QbE Distance- and PHOCbased methods, as well QbS HMM-Filler and BLSTM-CTC. Formal presentation and developments are accompanied by simple illustrative examples and/or

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Beyond Search Applications of Probabilistic Indexing,hat result from the PrIx process are not image transcripts, but they provide very rich probabilistic information about the text rendered in the images and image regions or locations. This chapter presents approaches to exploit this information to go beyond information search applications. Specifical

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Large-scale Systems and Applications,fast and effective access to the textual contents of many important series of historical manuscripts. We cover in detail five of these systems, which support large series provided by the ., the ., the ., and the . (.). All these large-scale demonstrators are publicly available through the Internet a

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Conclusion and Outlook, can also be applied using the new, promising models for optical modeling in HTR which are gaining increasing popularity. Finally, we also suggest how the ideas proposed in this book could be applied to other domains, where images are not necessarily of handwritten text.