Experimental Evaluation of a Trainable Scribble Recognizer for Calligraphic Interfaces

This paper describes a trainable recognizer for hand-drawn sketches using geometric features. We compare three different learning algorithms and select the best approach in terms of cost-performance ratio. The algorithms employ classic machine-learning techniques using a clustering approach. Experimental results show competing performance (95.1%) with the non-trainable recognizer (95.8%) previously developed, with obvious gains in flexibility and expandability. In addition, we study both their classification and learning performance with increasing number of examples per class.

[1]  J. R. Quinlan.  Discovering rules by induction from large collections of examples Intro-ductory readings in expert s , 1979 .

[2]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[3]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[4]  Manfred K. Warmuth,et al.  The weighted majority algorithm , 1989, 30th Annual Symposium on Foundations of Computer Science.

[5]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[6]  Joaquim A. Jorge,et al.  Experimental evaluation of an on-line scribble recognizer , 2001, Pattern Recognit. Lett..

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Ching Y. Suen,et al.  The State of the Art in Online Handwriting Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Donald Michie,et al.  Expert systems in the micro-electronic age , 1979 .

[10]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[11]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[12]  U. Fayyad.  On the induction of decision trees for multiple concept learning , 1991 .

[13]  Leonidas J. Guibas,et al.  Finding extremal polygons , 1982, STOC '82.

[14]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[15]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[16]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[17]  Herbert Freeman,et al.  Determining the minimum-area encasing rectangle for an arbitrary closed curve , 1975, CACM.

[18]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[19]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[20]  Dean Rubine,et al.  Specifying gestures by example , 1991, SIGGRAPH.

[21]  David P. Dobkin,et al.  Finding Extremal Polygons , 1985, SIAM J. Comput..

[22]  Joseph O'Rourke,et al.  Computational Geometry in C: Polygon Partitioning , 1998 .

[23]  Takayuki Dan Kimura,et al.  Recognizing multistroke geometric shapes: an experimental evaluation , 1993, UIST '93.

[24]  John Mingers,et al.  Rule Induction with Statistical Data—A Comparison with Multiple Regression , 1987 .

[25]  Donato Malerba,et al.  A Further Comparison of Simplification Methods for Decision-Tree Induction , 1995, AISTATS.

[26]  Bojan Cestnik,et al.  Estimating Probabilities: A Crucial Task in Machine Learning , 1990, ECAI.

[27]  J. Ross Quinlan,et al.  Learning Efficient Classification Procedures and Their Application to Chess End Games , 1983 .