Sketch recognition via string kernel

Sketch recognition is one of the essential step of sketch understanding. Challenge in sketch recognition is the variation and imprecision present in sketch. Free drawing styles of sketching make it difficult to build a robust sketch recognition system. This paper proposes a novel recognition approach that can recognize primitive shapes, as well as combinations of these primitives. The approach is independent of stroke order, number, as well as invariant to size and aspect ratio of sketch. Feature string is used to represent primitives. We defined a similarity measure on these feature strings that counts common substrings in two input strings, which is referred to as the string kernel in the field of kernel methods. Support vector machine(SVM) is then trained with labeled examples to handle the task of classification. The experiment on hand drawn digital circuit diagrams shows that our system can recognize sketching efficiently and robustly.