Issues and Challenges of Intelligent Systems and Computational Intelligence by László T. Kóczy Claudiu R. Pozna & Janusz Kacprzyk

Author:László T. Kóczy, Claudiu R. Pozna & Janusz Kacprzyk
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Each version of the FUBAR algorithm is handling 26 different single-stroke symbols without the use of dictionaries, learning or predictive methods.

The results for Palm’s Graffiti single-stroke recognition algorithm are from the study of Fleetwood et al. [7]. They stated that the average recognition rate for Palm’s Graffiti was 91 %; and 98 % for the virtual keyboard of the same device.

Graffiti 2 was the extended version of Graffiti in which the letters “i”, “t” and “x” could be written with multiple strokes. The accuracy of Graffiti 2 was investigated in the study of Költringer and Grechenig [8] in which the algorithm reached 86.03 % recognition rate.

The $1 algorithm was presented by Wobbrock et al. [9]. The method reached 97 % average recognition rate for 16 different single-stroke gestures.

As an improved, multi-stroke version of $1 algorithm Anthony and Wobbrock presented $N method, which reached 93.7 % average recognition rate for 20 multi-stroke symbols [10].

The FUBAR system with different modifications in most cases reached recognition rates which are well over 97 % user acceptance threshold defined by LaLomia [ct. 1] and in most of the cases it has outstanding results in recognition rate and computational complexity compared to other recognizers.

We have plans to create rule-base for different alphabets such as old Hungarian runes and Greek letters.

Another extension for the system is under development to optimize the rule-base and the shape of fuzzy grids with evolutionary algorithms to improve the accuracy of the system.

Advantages will be also investigated for the application of different dictionary-like and predictive methods to give the system a capability of context-based decision making.

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