Invariant Handwriting Features Useful in Cursive-Script Recognition

The within-writer variability of handwriting forms one of the problems in the automatic recognition of cursive script. Variability can be handled by choosing handwriting features based upon the process of handwriting generation or upon computational models. Handwriting patterns are represented by a sequence of motor actions, i.e., “strokes”, which can be identified by invariant segmentation. Each stroke is characterized by features related to motor memory parameters which can be identified by their high signal-to-noise ratios.

[1]  David W. Green,et al.  Context and motor control in handwriting , 1983 .

[2]  Réjean Plamondon,et al.  Computer processing of handwriting , 1990 .

[3]  A. W. Ellis.  Normality and pathology in cognitive functions , 1982 .

[4]  Sebastiano Impedovo,et al.  Frontiers in Handwriting Recognition , 1994 .

[5]  A. Thomassen,et al.  Invariants in Handwriting: The Information Contained in a Motor Program , 1986 .

[6]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[7]  A. Thomassen,et al.  Time, Size and Shape in Handwriting: Exploring Spatio-temporal Relationships at Different Levels , 1985 .

[8]  D. Margolin.  The neuropsychology of Writing and Spelling: Semantic, Phonological, Motor, and Perceptual Processes , 1984, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[9]  Drew H. Abney,et al.  Journal of Experimental Psychology : Human Perception and Performance Influence of Musical Groove on Postural Sway , 2015 .

[10]  P. Matthews.  Information processing in motor control and learning George E. Stelmach (ed.). Academic Press, New York (1978) 315 pp. $21.00 , 1979, Neuroscience.

[11]  A. J. Jerri.  Correction to "The Shannon sampling theorem—Its various extensions and applications: A tutorial review" , 1979 .

[12]  Hans-Leo Teulings,et al.  Digital recording and processing of handwriting movements , 1984 .

[13]  Lambert Schomaker,et al.  Between-letter context effects in handwriting trajectories , 1986 .

[14]  P Viviani,et al.  Segmentation and coupling in complex movements. , 1985, Journal of experimental psychology. Human perception and performance.

[15]  Gerard P. van Galen,et al.  Handwriting: Issues for a psychomotor theory ☆ , 1991 .

[16]  M. Kadirkamanathan,et al.  A SCALE-SPACE FILTERING APPROACH TO STROKE SEGMENTATION OF CURSIVE SCRIPT , 1990 .

[17]  LAMBERT R. B. SCHOMAKER,et al.  A computational model of cursive handwriting , 1987 .

[18]  Pietro Morasso,et al.  How a discontinuous mechanism can produce continuous patterns in trajectory formation and handwriting , 1983 .

[19]  F. Michel,et al.  Etude experimentale de la vitesse du geste graphique , 1971 .

[20]  G. Stelmach.  Information processing in motor control and learning , 1978 .

[21]  Hans-Leo Teulings,et al.  A DESCRIPTION OF HANDWRITING IN TERMS OF MAIN AXES , 1989 .

[22]  Lambertus Schomaker,et al.  Feature description of optically scanned handwritten words. I , 1993 .

[23]  F. J. Maarse,et al.  Produced and perceived writing slant: difference between up and down strokes. , 1983, Acta psychologica.

[24]  John A. Michon,et al.  Time, Mind, and Behavior , 1985, Springer Berlin Heidelberg.

[25]  Rodney Cotterill,et al.  Models of brain function , 1989 .

[26]  Lambertus Schomaker,et al.  Description of on-line script using Hollerbach's generation model. , 1993 .

[27]  Lambertus Schomaker,et al.  Stroke- versus Character-based Recognition of On-line, Connected Cursive Script , 1991 .

[28]  Hans-Leo Teulings,et al.  Constancy in stationary and progressive handwriting , 1983 .

[29]  Réjean Plamondon,et al.  An evaluation of motor models of handwriting , 1989, IEEE Trans. Syst. Man Cybern..

[30]  A. J. Jerri.  The Shannon sampling theorem—Its various extensions and applications: A tutorial review , 1977, Proceedings of the IEEE.

[31]  Lambert Schomaker,et al.  Invariant properties between stroke features in handwriting. , 1993, Acta psychologica.

[32]  J. Simon,et al.  From Pixels to Features III: Frontiers in Handwriting Recognition , 1992 .

[33]  A. Wing.  Response Timing in Handwriting , 1978 .

[34]  H. Kao,et al.  Graphonomics : contemporary research in handwriting , 1986 .

[35]  Lambert Schomaker,et al.  Proceedings of the Sixth International Conference on Handwriting and Drawing. , 1993 .

[36]  Hans-Leo Teulings.  A Handwriting Recognition System Based on Properties of the Human Motor System , 1990 .

[37]  Gerard P. van Galen,et al.  The independent monitoring of form and scale factors in handwriting , 1983 .

[38]  M. L. Meeks,et al.  MEASUREMENT OF DYNAMIC DIGITIZER PERFORMANCE , 1990 .

[39]  C. Tappert.  An Adaptive System for Handwriting Recognition , 1986 .

[40]  H. Pick,et al.  Geometric transformations of handwriting as a function of instruction and feedback. , 1983, Acta psychologica.

[41]  P. Viviani,et al.  The law relating the kinematic and figural aspects of drawing movements. , 1983, Acta psychologica.

[42]  Y. Guiard.  On Fitts's and Hooke's laws: simple harmonic movement in upper-limb cyclical aiming. , 1993, Acta psychologica.

[43]  A. Wing.  The height of handwriting , 1980 .

[44]  Ching Y. Suen,et al.  Computer recognition and human production of handwriting , 1989 .

[45]  Lambert Schomaker,et al.  FITTS LAW AS A LOW-PASS FILTER EFFECT OF MUSCLE-STIFFNESS , 1992 .

[46]  A. Thomassen,et al.  Preparation of partly precued handwriting movements: The size of movement units in handwriting , 1983 .

[47]  D. Chaffin,et al.  An investigation of fitts' law using a wide range of movement amplitudes. , 1976, Journal of motor behavior.

[48]  M. Martlew,et al.  The Psychology of written language : developmental and educational perspectives , 1985 .