Alex Tormási

Single-Stroke Character Recognition with Fuzzy Method

In this paper an on-line single-stroke recognition method based on fuzzy logic is introduced. Each of the characters is defined by only one nine dimensional fuzzy rule. In addition to the low resource requirement the solution is able to satisfy many of the user’s current demands in handwriting recognizers, like speed and learning. Eight of the nine features are extracted using a four-by-four grid. For the learning phase we designed a new punish/reward bacterial evolutionary algorithm which tunes the character parameters represented by fuzzy sets.

Comparing the Efficiency of a Fuzzy Single-Stroke Character Recognizer with Various Parameter Values

In this paper the results of a study on the accuracy of a fuzzy logic-based single-stroke character recognizer are presented by refining various parameter values, such as resolution of the fuzzy grid and the minimum distance between sampled points.

Improving the accuracy of a fuzzy-based single-stroke character recognizer by antecedent weighting

In this chapter we present an improved version of the fuzzy based single-stroke character recognizer introduced in previous works. The modified recognition method is able to reach higher accuracy in the character recognition without any significant effect on the computational complexity of the algorithm. Different fuzzy rule and antecedent weighting techniques were successfully used to improve the efficiency of fuzzy systems especially in classification problems. The altered recognizer reached 99.49 % average recognition rate with 26 different single-stroke symbols (based on Palm’s Graffiti alphabet) without learning user-specific parameters or modifying the rule-base. The new algorithm has the same computational complexity as the original system does.

Fuzzy Single-Stroke Character Recognizer with Various Rectangle Fuzzy Grids

In this chapter we introduce the results of a formerly published FUBAR character recognition method with various fuzzy grid parameters. The accuracy and efficiency of the handwritten single-stroke character recognition algorithm with different sized rectangle (N \(\times \) M) fuzzy grids are investigated. The results are compared to other modified FUBAR algorithms and known commercial and academic recognition methods. Possible applications and further extensions are also discussed. This work is the extended and fully detailed version of a previously published abstract.

Improved Fuzzy-Based Single-Stroke Character Recognizer

In this paper we present two modified and improved versions of the formerly published Fuzzy-Based Single-Stroke Character Recognizer (FUBAR) algorithm. After introducing the original method, the study investigates the effects of two different improvements of the designed algorithm. The first extension is the use of symbol-dependent fuzzy grids to extract symbol features; the second one is the use of rule weights in hierarchical rule-bases. The accuracy and efficiency of the extended FUBAR algorithms are compared to previous results.

Dynamic fuzzy rule weight optimization for a Fuzzy Based Single-Stroke Character Recognizer

In this paper a dynamic fuzzy rule weighting method (DFW) combined with evolutionary optimization are presented for the formerly published Fuzzy Based Single-Stroke Character Recognizer (FUBAR) method. With the introduced rule weighting technique the consequent parts of the if...then... rules are calculated similarly to the original FUBAR method, but a dynamic fuzzy rule weight Wn([0,1]) described as a fuzzy set is applied to it in On·1/Wn(On) form, where On is the output of the rule. The membership functions of DFW-s are determined by bacterial evolutionary algorithm. The paper compares the results of the proposed new algorithm with other (formerly published) FUBAR algorithms and also with other commercial and academic single-stroke recognizers in terms of recognition accuracy and computational resources needed.

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

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

Experimenting with a New Population-Based Optimization Technique: FUNgal Growth Inspired (FUNGI) Optimizer.

In this paper the experimental results of a new evolutionary algorithm are presented. The proposed method was inspired by the growth and reproduction of fungi. Experiments were executed and evaluated on discretized versions of common functions, which are used in benchmark tests of optimization techniques. The results were compared with other optimization algorithms and the directions of future research with many possible modifications/extension of the presented method are discussed.

A Survey of the Applications of Fuzzy Methods in Recommender Systems

In the past half century of fuzzy systems they were used to solve a wide range of complex problems, and the field of recommendation is no exception. The mathematical properties and the ability to efficiently process uncertain data enable fuzzy systems to face the common challenges in recommender systems. The main contribution of this paper is to give a comprehensive literature overview of various fuzzy based approaches to the solving of common problems and tasks in recommendation systems. As a conclusion possible new areas of research are discussed.

Comparing the Properties of Meta-heuristic Optimization Techniques with Various Parameters on a Fuzzy Rule-Based Classifier

In this paper, the results of meta-heuristic optimization techniques with various parameter settings are presented. A formerly published Fuzzy-Based Recognizer (FUBAR): A fuzzy rule-based classification algorithm was used to analyze and evaluate the behavior of the used meta-heuristic optimization algorithms for rule-base optimization. Besides the reached accuracy, the execution time, the CPU load of the algorithms, and the effects of the shapes of the fuzzy membership functions in the initial rule-base are also investigated.