Learning symbol relation tree for online mathematical expression recognition
This addresses the challenge of accurate recognition for users of digital math input, though it appears incremental as it builds on existing methods.
The paper tackles the problem of recognizing online handwritten mathematical expressions by proposing a method that builds a symbol relation tree directly from stroke sequences, achieving expression recognition rates of 44.12% and 41.76% on CROHME 2014 and 2016 testing sets.
This paper proposes a method for recognizing online handwritten mathematical expressions (OnHME) by building a symbol relation tree (SRT) directly from a sequence of strokes. A bidirectional recurrent neural network learns from multiple derived paths of SRT to predict both symbols and spatial relations between symbols using global context. The recognition system has two parts: a temporal classifier and a tree connector. The temporal classifier produces an SRT by recognizing an OnHME pattern. The tree connector splits the SRT into several sub-SRTs. The final SRT is formed by looking up the best combination among those sub-SRTs. Besides, we adopt a tree sorting method to deal with various stroke orders. Recognition experiments indicate that the proposed OnHME recognition system is competitive to other methods. The recognition system achieves 44.12% and 41.76% expression recognition rates on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and 2016 testing sets.