Global Context for improving recognition of Online Handwritten Mathematical Expressions
This work addresses the challenge of accurate recognition for users of digital math input systems, but it is incremental as it builds on existing methods like deep bidirectional LSTMs and SRT representations.
The paper tackles the problem of recognizing online handwritten mathematical expressions by proposing a temporal classification method that addresses symbol segmentation, recognition, and relation classification simultaneously, achieving effectiveness on the CROHME datasets.
This paper presents a temporal classification method for all three subtasks of symbol segmentation, symbol recognition and relation classification in online handwritten mathematical expressions (HMEs). The classification model is trained by multiple paths of symbols and spatial relations derived from the Symbol Relation Tree (SRT) representation of HMEs. The method benefits from global context of a deep bidirectional Long Short-term Memory network, which learns the temporal classification directly from online handwriting by the Connectionist Temporal Classification loss. To recognize an online HME, a symbol-level parse tree with Context-Free Grammar is constructed, where symbols and spatial relations are obtained from the temporal classification results. We show the effectiveness of the proposed method on the two latest CROHME datasets.