Integration of Shape Context and Neural Networks for Symbol Recognition

Using shape matching within a k-nearest neighbor approach, shape context descrip- tor has been applied in several classification problems with outstanding results. However, the application of this framework on large datasets or online scenarios is challenging due to its computational cost. To overcome this limitations, we evaluate the use of shape context as in- put features for neural networks. We test the proposed method in a problem of recognition of handwritten mathematical symbols. For a total of 75 classes of symbols, we obtained a recog- nition rate of 89.8%, comparable with a k-nearest neighbor approach, but with reduced time complexity.