Graph Based Keyword Spotting
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Graph-based Keyword Spotting
Keyword Spotting (KWS) has been proposed as a flexible and more error-tolerant alternative to full transcriptions. In most cases, it allows to retrieve arbitrary query words in handwritten historical document.This comprehensive compendium gives a self-contained preamble and visually attractive description to the field of graph-based KWS. The volume highlights a profound insight into each step of the whole KWS pipeline, viz. image preprocessing, graph representation and graph matching.Written by two world-renowned co-authors, this unique title combines two very current research fields of graph-based pattern recognition and document analysis. The book serves as an attractive teaching material for graduate students, as well as a useful reference text for professionals, academics and researchers.
Graph-based Keyword Spotting
Keyword Spotting (KWS) has been proposed as a flexible and more error-tolerant alternative to full transcriptions. In most cases, it allows to retrieve arbitrary query words in handwritten historical document. This comprehensive compendium gives a self-contained preamble and visually attractive description to the field of graph-based KWS. The volume highlights a profound insight into each step of the whole KWS pipeline, viz. image preprocessing, graph representation and graph matching. Written by two world-renowned co-authors, this unique title combines two very current research fields of graph-based pattern recognition and document analysis. The book serves as an attractive teaching material for graduate students, as well as a useful reference text for professionals, academics and researchers.
Graph-Based Representations in Pattern Recognition
This book constitutes the refereed proceedings of the 12th IAPR-TC-15 International Workshop on Graph-Based Representation in Pattern Recognition, GbRPR 2019, held in Tours, France, in June 2019. The 22 full papers included in this volume together with an invited talk were carefully reviewed and selected from 28 submissions. The papers discuss research results and applications at the intersection of pattern recognition, image analysis, and graph theory. They cover topics such as graph edit distance, graph matching, machine learning for graph problems, network and graph embedding, spectral graph problems, and parallel algorithms for graph problems.