A comparison of some intuitionistic fuzzy similarity measures applied to handwritten arabic sentences recognition

Abstract

In this paper we present a comparison of intuitionistic fuzzy similarity measures applied to Arabic sentences recognition using an extract of the IFN/ENIT data set. Such comparison shows the importance of similarity measures choice for any field of research needing to match between patterns.

Intuitionistic Fuzzy Similarity Measures and Their Role in Classification

“…In this section, we describe a data set of Arabic sentences with intuitionistic fuzzy information. Thus, we apply fuzzy similarity measures from literature and the proposed semi-metric distance measures to recognize Arabic sentences [5,9]. In the following subsections, we describe these processes with more details.…”

“…Intuitionistic fuzzy similarity measures are crucial operators for comparison between two patterns and are used in many systems such as recognition systems, classification systems, etc. ; as examples, applications include handwritten Arabic sentences recognition [5,9], logic programming [3,4], decision-making problems [38], and medical diagnosis [27,37]. Despite the numerous works on IFSMs and the different propositions of similarity and distance measures, most of them did numerical applications on some artificial samples and did not use a large data set to show the validation of IFSMs for classifying patterns or another domain in which similarity can influence results.…”

Relationship between intuitionistic fuzzy similarity measures

“…The same authors proposed some distance measures and IFSMs in [20] based on L p metric. More details about IFSMs and distance measures between IFSs are presented in [21], [22]. In section two, we present IFSs definition followed by the definition of distance measures and similarity measures between IFSs from literature.…”

Recognizing Cursive Typewritten Text Using Segmentation‐Free System

“…Various classification methods and techniques have been applied in recognizing Arabic alphanumerical and text. These include Template Matching [ 25 ], Euclidean Distance [ 26 ], Neural Networks [ 27 ], Fuzzy Logic [ 28 ], Genetic Algorithms [ 29 ], and Hidden Markov Models (HMMs) [ 30 ]. HMMs are statistical models which are widely and efficiently implemented among applications such as speech processing, online character recognition [ 31 ], and offline character recognition [ 32 ].…”