Dynamic Feature Selection for Structural Image Content Recognition

Structural image content recognition (SICR) aims to transcribe a two-dimensional structural image (e.g., mathematical expression, chemical formula, or music score) into a token sequence. Existing methods are mainly encoder-decoder based and overlook the importance of feature selection and spatial relation extraction in the feature map. In this paper, we propose DEAL (shorted for Dynamic fEAture seLection) for SICR, which contains a dynamic feature selector and a spatial relation extractor as two cornerstone modules. Specifically, we propose a novel loss function and random exploration strategy to dynamically select useful image cells for target sequence generation. Further, we consider the positional and surrounding information of cells in the feature map to extract spatial relations. We conduct extensive experiments to evaluate the performance of DEAL. Experimental results show that DEAL outperforms other state-of-the-arts significantly.