caa a22 4500 445379472 CHVBK 20180317143013.0 cr unu---uuuuu 170323e20110301xx s 000 0 eng 10.1007/s10032-010-0118-4 doi (NATIONALLICENCE)springer-10.1007/s10032-010-0118-4 Grammar-based techniques for creating ground-truthed sketch corpora [Elektronische Daten] [Scott MacLean, George Labahn, Edward Lank, Mirette Marzouk, David Tausky] Although publicly available, ground-truthed corpora have proven useful for training, evaluating, and comparing recognition systems in many domains, the availability of such corpora for sketch recognizers, and math recognizers in particular, is currently quite poor. This paper presents a general approach to creating large, ground-truthed corpora for structured sketch domains such as mathematics. In the approach, random sketch templates are generated automatically using a grammar model of the sketch domain. These templates are transcribed manually, then automatically annotated with ground-truth. The annotation procedure uses the generated sketch templates to find a matching between transcribed and generated symbols. A large, ground-truthed corpus of handwritten mathematical expressions presented in the paper illustrates the utility of the approach. Springer-Verlag, 2010 MacLean Scott David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut Labahn George David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut Lank Edward David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut Marzouk Mirette David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut Tausky David David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/1(2011-03-01), 65-74 1433-2833 14:1<65 2011 14 10032 https://doi.org/10.1007/s10032-010-0118-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-010-0118-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- MacLean Scott David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut NATIONALLICENCE 700 1- Labahn George David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut NATIONALLICENCE 700 1- Lank Edward David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut NATIONALLICENCE 700 1- Marzouk Mirette David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut NATIONALLICENCE 700 1- Tausky David David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada aut NATIONALLICENCE 773 0- International Journal on Document Analysis and Recognition (IJDAR) Springer-Verlag 14/1(2011-03-01), 65-74 1433-2833 14:1<65 2011 14 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer