In pen-based military situation marking systems

recognizing hand-drawn symbols is confronted with several challenges

such as numerous classes of graphic symbols

high similarity between classes

and orientation variation of many rotatable symbols.A rotation free recognition paradigm is presented considering these difficultiesand

aiming at classifying an instance of a symbol as well as estimating its rotation angle. First

rotation invariant coarse classification is performed to narrow the range of candidate classes.Then the rotation angle between the unknown instance and the template instance is estimated. They can be rotationally aligned by compensating the rotation angle between them.Finally

fining classification methods can be applied to distinguish similar symbols.A novel Zernike moments-based descriptor

called DZM

was used to represent hand-drawn symbol samples.It combines the spatial distribution of sample points and their local direction information.By matching DZM features

both coarse classification and rotation angle estimation could be accomplished. Experimental results show that the proposed method outperforms the traditional Zernike moment method in both classification and rotation angle estimation of hand-drawn military situation marking symbols. This method can be applied effectively in rotation free recognition of online hand-drawn military marking symbols.