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This paper describes an approach for offline recognition of handwrittenmathematical symbols. The process of symbol recognition in this paper includessymbol segmentation and accurate classification for over 300 classes. Manymultidimensional mathematical symbols need both horizontal and verticalprojection to be segmented. However, some symbols do not permit to be projectedand stop segmentation, such as the root symbol. Besides, many mathematicalsymbols are structurally similar, specifically in handwritten such as 0 andnull. There are more than 300 Mathematical symbols. Therefore, designing anaccurate classifier for more than 300 classes is required. This paper initiallyaddresses the issue regarding segmentation using Simple Linear IterativeClustering (SLIC). Experimental results indicate that the accuracy of thedesigned kNN classifier is 84% for salient, 57% Histogram of Oriented Gradient(HOG), 53% for Linear Binary Pattern (LBP) and finally 43% for pixel intensityof raw image for 66 classes. 87 classes using modified LeNet represents 90%accuracy. Finally, for 101 classes, SqueezeNet ac

Offline handwritten mathematical symbol recognition utilising deep learning