Paper Info
Title
A Style Preserving Shape Representation Scheme for Handwritten Gesture Recognition
Abstract
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.
Year
DOI
Venue
2017
10.1109/ICAPR.2017.8593187
2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)
Keywords
Field
DocType
online signature representation scheme,rotation,error prone process,authentication supplements,personalized command gestures,handwritten input,writing style information,mobile phones,hand-held device,handwritten pen,handwritten gesture recognition,style preserving shape representation scheme,personalized command gesture recognition,Zernike moments
Computer vision,Authentication,Computer science,Gesture,Writing style,Gesture recognition,Zernike polynomials,Artificial intelligence,Invariant (mathematics),Trajectory,Lossless compression
Conference
ISBN
Citations 
PageRank 
978-1-5386-2242-1
0
0.34
References 
Authors
9
2
Name
Order
Citations
PageRank
P. Viswanath114811.77
V. Chandra Sekhar200.34