A handwriting-based equation editor
Proceedings of the 1999 conference on Graphics interface '99
On the use of benchmarks for measuring system performance
ACM SIGARCH Computer Architecture News
An on-line symbolic mathematics system using hand-printed two-dimensional notation
ACM '69 Proceedings of the 1969 24th national conference
Applying Compiler Techniques to Diagram Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Automatic Ground-Truth Generation for Skew-Tolerance Evaluation of Document Layout Analysis Methods
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
MathPad2: a system for the creation and exploration of mathematical sketches
ACM SIGGRAPH 2004 Papers
An initial evaluation of a pen-based tool for creating dynamic mathematical illustrations
ACM SIGGRAPH 2007 courses
Automatic Ground-truth Generation for Document Image Analysis and Understanding
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Automated OCR Ground Truth Generation
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
MathBrush: A System for Doing Math on Pen-Based Devices
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
MathBrush: a case study for pen-based interactive mathematics
SBM'08 Proceedings of the Fifth Eurographics conference on Sketch-Based Interfaces and Modeling
Analyzing sketch content using in-air packet information
Proceedings of the 16th international conference on Intelligent user interfaces
Is the iPad useful for sketch input?: a comparison with the tablet PC
Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling
Automated labeling of ink stroke data
Proceedings of the International Symposium on Sketch-Based Interfaces and Modeling
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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.