Abstract: In some aspects, a method includes performing optical character recognition (OCR) based on data corresponding to a document to generate text data, detecting one or more bounded regions from the data based on a predetermined boundary rule set, and matching one or more portions of the text data to the one or more bounded regions to generate matched text data. Each bounded region of the one or more bounded regions encloses a corresponding block of text. The method also includes extracting features from the matched text data to generate a plurality of feature vectors and providing the plurality of feature vectors to a trained machine-learning classifier to generate one or more labels associated with the one or more bounded regions. The method further includes outputting metadata indicating a hierarchical layout associated with the document based on the one or more labels and the matched text data.

Abstract: Method and system for detecting potentially perturbed nodes in a graph that comprises potentially perturbed nodes and clean nodes, comprising: calculating, for each of a plurality of nodes of the graph, a discrepancy value in respect of the node, wherein the discrepancy value for each node indicates a statistical discrepancy for classification probabilities associated with the node and classification probabilities associated with neighbouring nodes; fitting a statistical distribution for the discrepancy values for the clean nodes; determining a detection threshold for potentially perturbed nodes based on the statistical distribution; and identifying nodes having a discrepancy value greater than the detection threshold as potentially perturbed nodes.

Abstract: In some aspects, a method includes performing optical character recognition (OCR) based on data corresponding to a document to generate text data, detecting one or more bounded regions from the data based on a predetermined boundary rule set, and matching one or more portions of the text data to the one or more bounded regions to generate matched text data. Each bounded region of the one or more bounded regions encloses a corresponding block of text. The method also includes extracting features from the matched text data to generate a plurality of feature vectors and providing the plurality of feature vectors to a trained machine-learning classifier to generate one or more labels associated with the one or more bounded regions. The method further includes outputting metadata indicating a hierarchical layout associated with the document based on the one or more labels and the matched text data.