A New Algorithm and System for the Characterization of Handwriting Strokes with Delta-Lognormal Parameters

• Published in: IEEE transactions on pattern analysis and machine intelligence 2009-11, Vol.31 (11), p.2060-2072
• Main Authors: Djioua, M., Plamondon, R.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Automatic Data Processing - methods; Biological and medical sciences; Biometry; Computer science; control theory; systems; curve-fitting; Delta-Lognormal model; Dispersion; Exact sciences and technology; Extraction; Fundamental and applied biological sciences. Psychology; Handwriting; Human; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; kinematic theory; Kinematics; lognormal function; Models, Statistical; motor control; Motor drives; Movements; Neuromuscular; nonlinear regression; optimization; Parameter estimation; parameter extraction; Pattern analysis; Pattern recognition; Pattern Recognition, Automated - methods; Peripheral nervous system. Autonomic nervous system. Neuromuscular transmission. Ganglionic transmission. Electric organ; rapid movement; Reading; Signal to noise ratio; Strokes; Subtraction Technique; Testing; Timing; Vertebrates: nervous system and sense organs
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2008.264

In this paper, we present a new analytical method for estimating the parameters of delta-lognormal functions and characterizing handwriting strokes. According to the kinematic theory of rapid human movements, these parameters contain information on both the motor commands and the timing properties of a neuromuscular system. The new algorithm, called XZERO, exploits relationships between the zero crossings of the first and second time derivatives of a lognormal function and its four basic parameters. The methodology is described and then evaluated under various testing conditions. The new tool allows a greater variety of stroke patterns to be processed automatically. Furthermore, for the first time, the extraction accuracy is quantified empirically, taking advantage of the exponential relationships that link the dispersion of the extraction errors with its signal-to-noise ratio. A new extraction system which combines this algorithm with two other previously published methods is also described and evaluated. This system provides researchers involved in various domains of pattern analysis and artificial intelligence with new tools for the basic study of single strokes as primitives for understanding rapid human movements.