A Style Preserving Shape Representation Scheme for Handwritten Gesture Recognition

Handwritten pen or finger based inputs are very common, especially with hand-held devices like mobile phones or PDAs. Apart from the content, there is the writing style information embedded in to the handwritten input. They are useful as personalized command gestures, passwords or as authentication supplements. Since the input is assumed to be written by a finger on the touch screen of the hand-held device, the input considered is a trajectory over the (x,y, t) space where x and y are the spacial and t is the time co-ordinate. The hand held device can be held in a variety of ways while giving the input, and size of the input may also vary. Normalizing with respect to rotation is an error prone process. Also, the other important limitation is that the number of training examples could be very few. The paper presents a novel style preserving TRS (translation, rotation and scale) invariant representation scheme for the 3D trajectory data that use 2D Zernike moments only and is called the time varying Zernike moments (TVZMs). For this, we present a 2D representation of the 3D function and show that it is lossless. The proposed representation scheme is sensitive to both content and to the writing style. Experimentally it is shown that 2D TVZMs are superior than 3D Zernike moments which possess same invariance properties. Also a comparison is drawn with a recent online signature representation scheme. Hence, it may be a suitable one for personalized command gesture recognition.