Ushur, Inc.

Vijayendra Mysore Shamanna

Badri Nath

Henry Thomas Peter

Simha Sadasiva

Yashu Seth

Shaik Kamran Moinuddin

Kaushal Kishore Hebbar

Ravil Kashyap

Systems and methods are disclosed for automatically extracting relevant information from various source document that include tabular data. The tabular data in various forms can be received as mixed with other dissimilar data. Tabular data can appear in different orientations, document types, and can be fragmented horizontally or vertically. The proposed technique automatically detects table header data in certain regions of the received source document and associates values to the extracted headers. The proposed system is capable of combining different snippets of smaller tables into a single cohesive and monolithic table with headers designated by a set of keywords and all the values in the various columns (and/or rows) included under or along proper headers.

Automatically extracting tabular data included within a source document

Systems and methods are disclosed for automatically extracting keys and corresponding values in any type of source document. Extracting desired words from the tokens in any type of document is based on a uniform approach to represent the source document. This uniform representation encodes features of the desired tokens along with the neighborhood information so that values associated with a given key can be extracted. The disclosed technique learns the representation of tokens independent of source document type and the learned representation is then used to determine relationships between multiple tokens. The neighborhood information and position information are used to determine various relationships between keys and values.

Automatically extracting key-value data included in heterogeneous document types using graph representation learning

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Cupertino, CA, United States of America

Vijayendra Mysore Shamanna