Microsoft Technology Licensing, LLC

Microsoft Corporation

Aljosa Obuljen

Milos Lazarevic

Milos Raskovic

Milan Sesum

Drazen Zaric

Dragan Slaveski

Ivan Vujic

Nikola Terzic

Vanja Petrovic Tankovic

Sanjay Krupakar Bhat

Karim Taji Farouki

Katrika Marie Morris

Catherine William Neylan

Jennifer Michelstein Halberstam

Filip Panjevic

Tumu Sree Bharath

Naresh Jain

Milos Jovanovic

Dusan Lukic

Jeet Mukeshkumar Patel

Abhas Sinha

Andreja Ilic

Gopalakrishnan Venkateswaran

Marko Rakita

Aleksandar Sretenovic

Tamara Paunovic

Sasa Vuckovic

Ajit Kumar Singh

Dhiresh Kumar Nagwani

Non-limiting examples of the present disclosure describe processing that generates intelligent inferences of authoring from analysis of attributes associated with a digital file being imported in an application/service. Examples described herein are configured to work with any type of application/service including an authoring application/service. For instance, a request to import a digital file is received in an application/service. The application/service may be configured to analyze the digital file and generate authoring inferences based on an analysis of attributes of the digital file. For example, a conversion data model may be utilized to identify a file type of the digital file, analyze attributes of the identified digital file (e.g. content portions, layout, formatting, metadata, etc.) and output file data in a format that is tailored for the application/service based on authoring inferences. A converted representation of the digital file is surfaced in the application/service based on output of the file data.

Intelligent inferences of authoring from document layout and formatting

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.

Converting an image into a structured table

A dynamic presentation of contextually relevant content during an authoring experience. As a user writes about a topic, the authored content is analyzed to identify one or more keywords that may be used to identify, retrieve and present suggested content to the user. The suggested content may be received from one or more resources, such as a search engine, a data store associated with the user, social media resources or other local or remote files. Techniques described herein might also select the keywords from authored content based on a cursor position. As a result, the suggested content may change as the cursor moves to a new position in the authored content. In addition, techniques described herein provide a user interface control that allows for the selection and de-selection of one or more keywords, which allows a user to tailor the suggested content by toggling one or more controls.

Dynamic presentation of suggested content

A vector graphics classification engine and associated method for classifying vector graphics in a fixed format document is described herein and illustrated in the accompanying figures. The vector graphics classification engine defines a pipeline for categorizing vector graphics parsed from the fixed format document as font, text, paragraph, table, and page effects, such as shading, borders, underlines, and strikethroughs. Vector graphics that are not otherwise classified are designated as basic graphics. By sequencing the detection operations in a selected order, misclassification is minimized or eliminated.

Vector graphics classification engine

Determining relationships between graphical elements in a fixed format document is provided. Graphical element sizes and their relative positions may be analyzed to determine whether two or more graphical elements should be aggregated together or whether the graphical elements should belong to different graphical groups. Graphs and figures comprising objects that are absolutely positioned may be detected, as well as objects where inter-element positions need to be preserved from regular document flow. Additionally, background objects may be differentiated from regular text flow when the objects overlap with text.

Grouping fixed format document elements to preserve graphical data semantics after reflow by manipulating a bounding box vertically and horizontally

A paragraph alignment detection engine and a section reconstruction engine. The paragraph alignment detection engine determines the paragraph alignment of a paragraph and updates the paragraph alignment property of the paragraph in the data store for single line and multi-line paragraphs. The paragraph alignment detection engine employs per paragraph comparisons and relative comparisons to other paragraphs to determine the paragraph alignment of a single line paragraph. The paragraph alignment detection engine employs per paragraph comparisons and relative comparisons of the lines of a paragraph to determine the paragraph alignment of a multi-line paragraph. The section reconstruction engine minimizes the number of sections created in the flow format document by identifying the columns on each page, combining contiguous pages with the same column layout into a single section, and creating alternative objects to contain regions associated special cases in lieu of creating additional sections.

Paragraph alignment detection and region-based section reconstruction

A formula detection engine and associated method. The formula detection engine locates formulas within a fixed format document portion by identifying formula seeds. The formula detection engine creates and expands a boundary around the formula seed to define a formula area. To eliminate overlap with surrounding normal text, the formula area is divided into multiple formula areas based on vertical position and horizontal spacing between the formula elements. After being vertically ordered, horizontally overlapping formula areas are merged to reconstruct the formula as a flowable element.

Formula detection engine

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Belgrade, Republic of Serbia

Aljosa Obuljen