Recognition of handwritten mathematical expressions has been an important topic for many researchers for decades. It remains one of the most challenging and exciting areas in pattern recognition. In the recognition process of offline handwritten mathematical expressions, segmentation is the most important process. Problems in ambiguities of identifying superscript and subscript in complex offline mathematical expressions remain one of the most important problem. To the best of our knowledge little work has been done in the segmentation of offline handwritten mathematical expressions with respect to superscript and subscript. In this paper an efficient segmentation technique for superscript, subscript and main characters within offline handwritten mathematical expressions has been proposed. This technique is based on the generation of predictions for superscript, subscript and main characters within handwritten mathematical expressions, which helps for the reconstruction of mathematical expressions during the recognition process with their spatial interrelationship. The proposed system was conducted as an experiment with a database of 300 samples of scanned mathematical expressions that comprised 2,000 symbols out of which there were 31 different types of Mathematical Symbols. The classification of the elements was carried out by the K-NN-classifier based on density features. This experiment shows remarkable results. Keywords: Features extraction; K-NN classification; Mathematical Expressions (ME) Recognition; Segmentation Published at: IJTech Vol 6, No 3 (2015) DOI: http://dx.doi.org/10.14716/ijtech.v6i3.1069

Resolving ambiguity within mathematical symbols is essential for recognition of mathematical expressions. In this paper, we focus on the resolving ambiguities in mathematical symbols and propose a novel recognition technique that has been tested over large number of ambiguous mathematical symbols obtained from different categories including factoring formula, algebra identity, geometric progression, integral expressions, quadratic equations, area function, and geometric progression formulas for mathematical expressions. In this paper, we propose a novel feature extraction technique which helps in reducing the ambiguity within mathematical symbols. First, we have identified ambiguous isolated mathematical symbols from different categories of mathematical expressions. The features are extracted using feature extraction technique which includes Fourier descriptor and chain code. Two different classifiers SVM and K-NN are used for classification purpose. Fivefold cross validation technique has been used for computing recognition results. Overall recognition rate of 97.03 and 96.62% is achieved using SVM and K-NN classification respectively.

A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1. The input space is sometimes referred to as the universe of discourse. This paper further develops the fuzzy-based algorithm to add the feature of automatic membership function generation in the fuzzy logic module of the algorithm. From this context, a short review of related work in membership function generation is given, and rules associated with it have been incorporated. In this paper, a one step ahead to the nature of the fuzzy logic-based design, a fitness finding method has been proposed. This paper also evaluates the proposed algorithm for deriving membership function based on association rule using control parameters with its implementation. The algorithm is applied by considering a case study of share market data and results are analyzed and compared with the intuitive cases.