Link prediction Graph Neural Networks for structure recognition of Handwritten Mathematical Expressions
This addresses structure recognition in handwritten math for applications like educational tools, but it appears incremental as it builds on existing GNN and parsing methods.
The paper tackles the problem of recognizing Handwritten Mathematical Expressions by modeling them as graphs and using a Graph Neural Network for link prediction to refine the structure, showing promising performance in experiments.
We propose a Graph Neural Network (GNN)-based approach for Handwritten Mathematical Expression (HME) recognition by modeling HMEs as graphs, where nodes represent symbols and edges capture spatial dependencies. A deep BLSTM network is used for symbol segmentation, recognition, and spatial relation classification, forming an initial primitive graph. A 2D-CFG parser then generates all possible spatial relations, while the GNN-based link prediction model refines the structure by removing unnecessary connections, ultimately forming the Symbol Label Graph. Experimental results demonstrate the effectiveness of our approach, showing promising performance in HME structure recognition.