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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
A hidden-Markov-model (HMM)-based system for font-independent spotting of user-specified keywords in a scanned image is described. Word bounding boxes of potential keywords are extracted from the image using a morphology-based preprocessor. Feature vectors based on the external shape and internal structure of the word
We present a handwritten text Keyword Spotting (KWS) approach based on the combination of KWS methods using word-graphs (WGs) and character-lattices (CLs). It aims to solve the problem that WG-based models present for out of vocabulary (OOV) keywords: since there is no available information about them in the lexicon
Word spotting systems are intended to retrieve occurrences of a given keyword in document images without actually recognizing the full document content. As there is a trend towards segmentation-free word spotting methods, we propose a methodology to evaluate these methods by employing measures that take the quality of
student appearances, which are linked to extracted headshots to create a visual speaker index. Videos are augmented with time-aligned filtered keywords and phrases from highly inaccurate speech transcripts. An experimental user interface (UI) combines streaming videos, visual, and textual indices for browsing and searching
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