A new algorithm for recognition of mathematical symbols

Mathematical Expression Recognition (MER) is an important requirement in science because of the prevalence of the mathematical expressions in the science literature. The symbols are not lined up horizantally and their size may not be similar in MER in contrast with Standart Text Recognition (STR). Thus, recogniton of the mathematical expression can be very difficult in comparison with STR. So, academical studies on MER are goes on effectively today. In this thesis, a software system; which is composed of Symbol Segmentation, Symbol Recognition and Spatial Analysis steps; are proposed. Also, A new statistical feature family called Orphan-Pixel-Rate/Local-Orphan-Pixel-Rate (OPR/LOPR) are introduced. OPR/LOPR features are defined by how black pixels of the binary image encloses its white pixels. In this thesis, it will be shown that a feature vector mainly created by using OPR/LOPR can increase significantly the accuracy of recogniton. For that purpose, OPR/LOPR vector will be compared with two other common feature vectors.(3 x 3 Bitmap, wavelet) according to their results of recognition speed and accuracy after its classification with three different method (Kstar, MLP, KNN). This offline MER sistem have been implemented using Weka software packet based on Java.