Tools for the Efficient Generation of Hand-Drawn Corpora Based on Context-Free Grammars
dc.contributor.author | MacLean, Scott | en_US |
dc.contributor.author | Tausky, David | en_US |
dc.contributor.author | Labahn, George | en_US |
dc.contributor.author | Lank, Edward | en_US |
dc.contributor.author | Marzouk, Mirette | en_US |
dc.contributor.editor | Cindy Grimm and Joseph J. LaViola, Jr. | en_US |
dc.date.accessioned | 2014-01-28T18:04:21Z | |
dc.date.available | 2014-01-28T18:04:21Z | |
dc.date.issued | 2009 | en_US |
dc.description.abstract | In sketch recognition systems, ground-truth data sets serve to both train and test recognition algorithms. Unfortunately, generating data sets that are sufficiently large and varied is frequently a costly and time-consuming endeavour. In this paper, we present a novel technique for creating a large and varied ground-truthed corpus for hand drawn math recognition. Candidate math expressions for the corpus are generated via random walks through a context-free grammar, the expressions are transcribed by human writers, and an algorithm automatically generates ground-truth data for individual symbols and inter-symbol relationships within the math expressions. While the techniques we develop in this paper are illustrated through the creation of a ground-truthed corpus of mathematical expressions, they are applicable to any sketching domain that can be described by a formal grammar. | en_US |
dc.description.seriesinformation | EUROGRAPHICS Workshop on Sketch-Based Interfaces and Modeling | en_US |
dc.identifier.isbn | 978-3-905674-19-4 | en_US |
dc.identifier.issn | 1812-3503 | en_US |
dc.identifier.uri | https://doi.org/10.2312/SBM/SBM09/125-132 | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.5.5 [Computing Methodologies]: Pattern Recognition-Implementation | en_US |
dc.title | Tools for the Efficient Generation of Hand-Drawn Corpora Based on Context-Free Grammars | en_US |
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