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Document structure extraction has been a widely researched area for decades.Recent work in this direction has been deep learning-based, mostly focusing onextracting structure using fully convolution NN through semantic segmentation.In this work, we present a novel multi-modal approach for form structureextraction. Given simple elements such as textruns and widgets, we extracthigher-order structures such as TextBlocks, Text Fields, Choice Fields, andChoice Groups, which are essential for information collection in forms. Toachieve this, we obtain a local image patch around each low-level element(reference) by identifying candidate elements closest to it. We process textualand spatial representation of candidates sequentially through a BiLSTM toobtain context-aware representations and fuse them with image patch featuresobtained by processing it through a CNN. Subsequently, the sequential decodertakes this fused feature vector to predict the association type betweenreference and candidates. These predicted associations are utilized todetermine larger structures through connected components analysis. Experimentalresults show the effectiveness of our approach achieving a recall of 90.29%,73.80%, 83.12%, and 52.72% for the above structures, respectively,outperforming semantic segmentation baselines significantly. We show theefficacy of our method through ablations, comparing it against using individualmodalities. We also introduce our new rich human-annotated Forms Dataset.

Multi-Modal Association based Grouping for Form Structure Extraction