Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Ground-Truthed Mathematical Character and Symbol Image Database
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
Towards Handwritten Mathematical Expression Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Structural matching of 2D electrophoresis gels using deformed graphs
Pattern Recognition Letters
Grammar-based techniques for creating ground-truthed sketch corpora
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
Recognition of online handwritten mathematical expressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A shape-based layout descriptor for classifying spatial relationships in handwritten math
Proceedings of the 2013 ACM symposium on Document engineering
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Mathematical expression recognition is one of the challenging problems in the field of handwritten recognition. Public datasets are often used to evaluate and compare different computer solutions for recognition problems in several domains of applications. However, existing public datasets for handwritten mathematical expressions and symbols are still scarce both in number and in variety. Such scarcity makes large scale assessment of the existing techniques a difficult task. This paper proposes a novel approach, based on expression matching, for generating ground-truthed exemplars of expressions (and, therefore, of symbols). Matching is formulated as a graph matching problem in which symbols of input instances of a manually labeled model expression are matched to the symbols in the model. Pairwise matching cost considers both local and global features of the expression. Experimental results show achievement of high accuracy for several types of expressions, written by different users.