Identification of the initial rule-base of a multi-stroke fuzzy-based character recognition method with meta-heuristic techniques

Alex Tormási,

László T. Kóczy


This paper summarizes the basic concept of the designed a fuzzy-based character recognition algorithm family and the results of the optimization of its rule-base with two various meta-heuristic methods, the Imperialist Competitive Algorithm and the bacterial evolutionary algorithm. The results are presented and compared with two other methods from literature after a short overview of the recognition algorithm.

Słowa kluczowe: fuzzy systems, character recognition, metaheuristic optimization

LaLomia M.J., User acceptance of handwritten recognition accuracy, Companion Proc. CHI ’94, New York 1994, 107.

Zadeh L.A., Fuzzy sets, Inf. Control, 83, 1965, 338-35.

Mamdani E.H., Assilian S., An experiment in linguistic synthesis with a fuzzy logic controller, International Journal of Man-Machine Studies, Vol. 7, 1975, 1-13.

Takagi T., Sugeno M., Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-15, 1985, 116-132.

Tormási A., Botzheim J., Single-stroke character recognition with fuzzy method, New Concepts and Applications in Soft Computing SCI, Vol. 417, V.E. Balas et al. (eds.), 2012, 27-46.

Tormási A., Kóczy T.L., Improving the Accuracy of a Fuzzy-Based Single-Stroke Character Recognizer by Antecedent Weighting, Proc. 2nd World Conference on Soft Computing, Baku 2012, 172-178.

Tormási A., Kóczy T.L., Fuzzy-Based Multi-Stroke Character Recognizer, Preprints of the Federated Conference on Computer Science and Information Systems, Kraków 2013, 675-678.

Tormási A., Kóczy T.L., Comparing the efficiency of a fuzzy single-stroke character recognizer with various parameter values, Proc. IPMU 2012, Part I. CCIS, Vol. 297, S. Greco et al. (eds.), 2012, 260–269.

A. Tormasi, and Kóczy T.L., Efficiency and accuracy analysis of a fuzzy single-stroke character recognizer with various rectangle fuzzy grids, Proc. CSCS ’12, Szeged 2012, 54-55.

Sugeno M., Griffin F.M., Bastian A., Fuzzy hierarchical control of an unmanned helicopter, Proc. IFSA ’93, Seoul 1993, 1262-1265.

Kóczy T.L., Hirota K., Approximate inference in hierarchical structured rule-bases, Proc. IFSA ’93, Seoul 1993, 1262-1265.

Tormási A., Kóczy T.L., Improving the Efficiency of a Fuzzy-Based Single-Stroke Character Recognizer with Hierarchical Rule-Base, Proc. 13th IEEE International Symposium on Computational Intelligence and Informatics, Óbuda 2012, 421-426.

Atashpaz-Gargari E., Lucas C., Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition, Proc. 2007 IEEE Congress on Evolutionary Computation, 7, Singapore 2007, 4661-4666.

Nawa N.E., Furuhashi T., Fuzzy system parameters discovery by bacterial evolutionary algorithm, IEEE Transactions on Fuzzy Systems, 7(5), 1999, 608-616.

Költringer T., Grechenig T., Comparing the Immediate Usability of Graffiti 2 and Virtual Keyboard, Proc. CHI EA’04, New York 2004, 1175-1178.

Anthony L., Wobbrock J.O., A Lightweight Multistroke Recognizer for User Interface Prototypes, Proc. GI 2010, Ottawa 2010, 245-252.

Fleetwood M.D. et al., An evaluation of text-entry in Palm OS – Graffiti and the virtual keyboard, Proc. HFES’02, Santa Monica, CA, 2002, 617-621.

Wobbrock J.O., Wilson A.D., Li Y., Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes, Proc. UIST ‘07. ACM Press, New York 2007, 159-168.

Monteiro C., Leal J.P., Managing experiments on cognitive processes in writing with HandSpy, Computer Science and Information Systems, Vol. 10, No. 4, Novi Sad 2013, 1747-1773.