Grammar-based techniques for creating ground-truthed sketch corpora

Authors:
Scott MacLean;George Labahn;Edward Lank;Mirette Marzouk;David Tausky
Affiliations:
University of Waterloo, David R. Cheriton School of Computer Science, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada;University of Waterloo, David R. Cheriton School of Computer Science, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada;University of Waterloo, David R. Cheriton School of Computer Science, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada;University of Waterloo, David R. Cheriton School of Computer Science, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada;University of Waterloo, David R. Cheriton School of Computer Science, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada
Venue:
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
Year:
2011

Citing 0
Cited 5

Automatic labeling of handwritten mathematical symbols via expression matching

GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Writing on clouds

CICM'12 Proceedings of the 11th international conference on Intelligent Computer Mathematics
A shape-based layout descriptor for classifying spatial relationships in handwritten math

Proceedings of the 2013 ACM symposium on Document engineering
Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models

Pattern Recognition Letters
Sketched symbol recognition using Latent-Dynamic Conditional Random Fields and distance-based clustering

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.