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# Dynamic Random Forest for the Recognition of Arabic Handwritten Mathematical Symbols with A

Mathematics has a number of characteristics which distinguish it from conventional text and make it a challenging
area for recognition. This include principally its two dimensional structure and the diversity of used symbols, especially in
Arabic context. Recognition of mathematical formulas requires solving three sub problems: segmentation, the symbol
recognition and finally the symbol arrangement analysis. In this paper we will focus on the Arabic mathematical symbol
recognition step. This is a challenging task due to the large symbol set with many similar looking symbols used in Arabic
mathematics and also the great variability found in human writing. The strength of the selected features and the effectiveness
of the classifier are the two key factors determining the performance of a handwritten symbols recognition System .In this
paper we proposed a novel Shape Context (SH) descriptor and explored its combination with a modified Chain Code
Histogram (CCH) and a Histogram of Oriented Gradient (HOG) at the level of descriptors extraction. For the classification
we used a Dynamic Random Forest (DRF) model which has the advantage of efficiently modelling the interaction among trees
to determine the right prediction. The results carried out Handwritten Arabic Mathematical Dataset (HAMF) show that the
DRF proves a significant improvement in terms of accuracy compared to the standard static RF and Support Vector Machines
(SVM).

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[27] Zanibbi R. and Blostein D., Recognition and Retrieval of Mathematical Expressions, International Journal of Document Analysis and Recognition, vol. 15, no. 4, pp. 331-357, 2012. Dynamic Random Forest for the Recognition of Arabic Handwritten Mathematical ... 575 Ibtissem Ali Received the Diploma of computer science Engineering and Diploma of master respectively in 2010 and 2013 from the National Engineering School of Sousse - Tunisia. She is currently a PH D student and member of research laboratory LATIS (Laboratory of Advanced Technology and Intelligent Systems) team of analysis and processing of document. Her research interests include handwritten mathematical recognition, Arabic optical character recognition, document analysis, computer vision and pattern recognition. Mohamed Mahjoub is an associate professor in Signal and Image processing at the National Engineering School of Sousse (ENISo) and member of the Laboratory of Advanced Technology and Intelligent Systems (LATIS). His research interests include dynamic Bayesian network, computer vision, pattern recognition, HMM and data retrieval. He is a member of IEEE and his main results have been published in international journals and conferences.