  This paper describes an approach for offline recognition of handwritten
mathematical symbols. The process of symbol recognition in this paper includes
symbol segmentation and accurate classification for over 300 classes. Many
multidimensional mathematical symbols need both horizontal and vertical
projection to be segmented. However, some symbols do not permit to be projected
and stop segmentation, such as the root symbol. Besides, many mathematical
symbols are structurally similar, specifically in handwritten such as 0 and
null. There are more than 300 Mathematical symbols. Therefore, designing an
accurate classifier for more than 300 classes is required. This paper initially
addresses the issue regarding segmentation using Simple Linear Iterative
Clustering (SLIC). Experimental results indicate that the accuracy of the
designed kNN classifier is 84% for salient, 57% Histogram of Oriented Gradient
(HOG), 53% for Linear Binary Pattern (LBP) and finally 43% for pixel intensity
of raw image for 66 classes. 87 classes using modified LeNet represents 90%
accuracy. Finally, for 101 classes, SqueezeNet ac
