Visual Parsing with Query-Driven Global Graph Attention (QD-GGA): Preliminary Results for Handwritten Math Formula Recognition

Abstract

We present a new visual parsing method based on convolutional neural networks for handwritten mathematical formulas. The Query-Driven Global Graph Attention (QD-GGA) parsing model employs multi-task learning, and uses a single feature representation for locating, classifying, and relating symbols. First, a Line-Of-Sight (LOS) graph is computed over the handwritten strokes in a formula. Second, class distributions for LOS nodes and edges are obtained using query-specific feature filters (i.e., attention) in a single feed-forward pass. Finally, a Maximum Spanning Tree (MST) is extracted from the weighted graph. Our preliminary results show that this is a promising new approach for visual parsing of handwritten formulas. Our data and source code are publicly available.