Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Grammar-based techniques for creating ground-truthed sketch corpora
International Journal on Document Analysis and Recognition - Special Issue on Performance Evaluation
Automatic labeling of handwritten mathematical symbols via expression matching
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Description and Discrimination of Planar Shapes Using Shape Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Earth Mover's Distance in the Bag-of-Visual-Words Model for Mathematical Symbol Retrieval
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
| Hi-index | 0.00 |
We consider the difficult problem of classifying spatial relationships between symbols and subexpressions in handwritten mathematical expressions. We first improve existing geometric features based on bounding boxes and center points, normalizing them using the distance between the centers of the two symbols or subexpressions in question. We then propose a novel feature set for layout classification, using polar histograms computed over points in handwritten strokes. A series of experiments are presented in which a Support Vector Machine is used with these new features to classify spatial relationships of five types in the MathBrush corpus (horizontal, superscript, subscript, below, and inside (e.g. in a square root)). The normalized geometric features provide an improvement over previously published results, while the shape-based features provide a natural representation with results comparable to those for the geometric features. Combining the features produced a very small improvement in accuracy.