Abstract: A system for converting an image of an unstructured table into a structured table is provided. The system may comprise a memory storing machine readable instructions. The system may include a processor to receive an image of a unstructured table and convert the image of the unstructured table into a structured table. Converting the image of the unstructured table into the structured table may include providing cell mapping and low confidence determination to highlight potentially misconverted content. The low confidence determination may be based on a first input and a second input. The processor may export the structured table, upon validation, to an application that supports structured tables.

Abstract: A system for converting an image of an unstructured table into a structured table is provided. The system may comprise a memory storing machine readable instructions. The system may include a processor to receive an image of a unstructured table and convert the image of the unstructured table into a structured table. Converting the image of the unstructured table into the structured table may include providing cell mapping and low confidence determination to highlight potentially misconverted content. The low confidence determination may be based on a first input and a second input. The processor may export the structured table, upon validation, to an application that supports structured tables.

Abstract: The present disclosure is directed to a method for optically recognizing a table and converting that recognized table to a digitized format. In particular, the present disclosure relates to a method of optically recognizing and identifying a table generally, individual cells within the table, the data embedded within each cell, as well as the original table format, including shading, cell borders, colors, and effects. Accordingly, such digitization of an optically recognized table, in whole or in part, as printed on a document or other media allows users to easily and quickly capture information as originally arranged without having to manually re-create a table and enter data into the re-created table.

Abstract: Detection of a document type of a document is provided. An application such as a document processing application analyzes a document to detect features of the document and pages of the document, based on an action selecting the document. The features include declarative and separative features. A type of the pages is detected based on the features. The type of the pages and values of the features of the document are aggregated to identify the document type of the document.

Abstract: A multi-level list detection engine. The multi-level list detection engine detects text obtained from a fixed format document that is formatted as a static multi-level list and creates a dynamic multi-level list object in a flow format document. The resulting dynamic multi-level list object automatically updates as the end user edits the multi-level list in the flow format document. The multi-level list detection engine identifies list elements in the fixed format text based on the presence of a list identifier. The list elements are grouped into lists based on the properties of each list element relative to other list elements. List elements are then assigned to a list level based on the relative properties of the list elements within a list. Finally, level list assignments are verified and corrected, the levels are merged, as necessary, and the lists are consistently formatted as appropriate to create a final well-formed dynamic multi-level list object.

Abstract: Detection of a document type of a document is provided. An application such as a document processing application analyzes a document to detect features of the document and pages of the document, based on an action selecting the document. The features include declarative and separative features. A type of the pages is detected based on the features. The type of the pages and values of the features of the document are aggregated to identify the document type of the document.

Abstract: Technologies are described herein for inferring the layout intent associated with explicitly formatted document elements in a document. The layout type of a document having explicitly formatted document elements is determined. Once the layout type for the document has been determined, the layout intent of explicitly formatted document elements in the document may be determined based, at least in part, on the determined layout type of the document. Heuristic algorithms and/or machine learning classifiers may determine the layout intent of the explicitly formatted document elements in the document. An intent-based document is then created using the inferred layout intent for some or all of the explicitly formatted document elements in the document. The intent-based document may then be provided to an intent-based rendering or authoring application for rendering based upon the inferred layout intent.

Abstract: Technologies are described herein for obtaining and processing intent-based feedback. Authored content may be generated by the use of one or more layouts. The generated layouts may then be utilized to present the content to the author or other user. The author may further provide feedback to request the generated layout be adjusted or re-generated. The feedback may include overriding feedback that overrides the intent interpretation used in the layout, and/or intent feedback that changes or adds more intent data for the content data.

Abstract: A multi-level list detection engine. The multi-level list detection engine detects text obtained from a fixed format document that is formatted as a static multi-level list and creates a dynamic multi-level list object in a flow format document. The resulting dynamic multi-level list object automatically updates as the end user edits the multi-level list in the flow format document. The multi-level list detection engine identifies list elements in the fixed format text based on the presence of a list identifier. The list elements are grouped into lists based on the properties of each list element relative to other list elements. List elements are then assigned to a list level based on the relative properties of the list elements within a list. Finally, level list assignments are verified and corrected, the levels are merged, as necessary, and the lists are consistently formatted as appropriate to create a final well-formed dynamic multi-level list object.