Microsoft Technology Licensing, LLC

Microsoft Corporation

Milos Lazarevic

Milos Raskovic

Milan Sesum

Aljosa Obuljen

Drazen Zaric

Dragan Slaveski

Nikola Terzic

Vanja Petrovic Tankovic

Sanjay Krupakar Bhat

Catherine William Neylan

Filip Panjevic

Aleksandar Tomic

Tumu Sree Bharath

Naresh Jain

Jeet Mukeshkumar Patel

Dusan Lukic

Abhas Sinha

Ivan Vujic

Marko Rakita

Elizabeth Jeanne Sheldon

Gopalakrishnan Venkateswaran

Sasa Vuckovic

Ajit Kumar Singh

Dhiresh Kumar Nagwani

Marija Antic

Aleksandar Sretenovic

Tamara Paunovic

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 color coding engine and a comparison engine are provided. A color coding engine may be utilized to detect logical layout object attributes in a flow format document and apply a unique color to textual elements associated with each logical layout object attribute. The resulting color coded document may be saved as a target flow format document. The target flow format document may be converted to a fixed format document and then converted by a conversion engine to a flow format document. The resulting converted flow format document may be saved as an output flow format document. A comparison engine may be utilized to compare the output flow format document and the target flow format document to determine if layout information has been properly preserved in the document conversion process.

Color coding of layout structure elements in a flow format document

Detection of East Asian layout features and reconstruction of East Asian layout features is provided. Vertically written text in the fixed format document is detected and rotated for layout analysis. After layout analysis, the rotated text is rotated back and restructured in a flow format document. When a plurality of characters is written horizontally in a vertical line of text, vertically overlapping text runs are detected, designated as horizontal-in-vertical text, and are restructured as horizontal-in-vertical text in a flow format document. Lines of text are analyzed for attributes of a ruby line and are designated as ruby text, associated with corresponding text in a ruby base line, and restructured as ruby text in a flow format document. Text in a fixed format document is analyzed for detection of a particular East Asian language so that a font for the language is designated in a flow format document.

Detection and reconstruction of East Asian layout features in a fixed format document

Embodiments of the present disclosure provide for analyzing paragraphs in a fixed format document to determine style clusters or groupings of each paragraph. In certain embodiments, the paragraphs are grouped into style clusters based on a first property. Each style cluster is then further divided into sub-groups based on a second property. Once the sub-groups have been determined, a third property associated with each paragraph in each sub-group is normalized based on a dominant one of the at least the third property.

Paragraph property detection and style reconstruction engine

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

Detection and reconstruction of east asian layout features in a fixed format document

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Belgrade, Republic of Serbia

Milos Lazarevic