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This paper proposes a method for recognizing online handwritten mathematicalexpressions (OnHME) by building a symbol relation tree (SRT) directly from asequence of strokes. A bidirectional recurrent neural network learns frommultiple derived paths of SRT to predict both symbols and spatial relationsbetween symbols using global context. The recognition system has two parts: atemporal classifier and a tree connector. The temporal classifier produces anSRT by recognizing an OnHME pattern. The tree connector splits the SRT intoseveral sub-SRTs. The final SRT is formed by looking up the best combinationamong those sub-SRTs. Besides, we adopt a tree sorting method to deal withvarious stroke orders. Recognition experiments indicate that the proposed OnHMErecognition system is competitive to other methods. The recognition systemachieves 44.12% and 41.76% expression recognition rates on the Competition onRecognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and2016 testing sets.

Learning symbol relation tree for online mathematical expression recognition