Microsoft Technology Licensing, LLC

Milan Sesum

Milos Lazarevic

Milos Raskovic

Aljosa Obuljen

Drazen Zaric

Dragan Slaveski

Nikola Terzic

Filip Panjevic

Ivan Vujic

Vanja Petrovic Tankovic

Nenad Bozidarevic

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

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

A borderless table detection engine and associated method for identifying borderless tables appearing in data extracted from a fixed format document. Due to the lack of visible borders, reliable automated detection of a borderless table is difficult. The borderless table detection engine uses whitespace, rather than content, to detect borderless table candidates. Applying heuristic analysis, the borderless table detection engine discards borderless table candidates with a layout that lacks sufficient characteristics of a table and is unlikely to be a valid borderless table.

Borderless table detection 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

One or more components of an image document may be detected and extracted in order to create a flow document from the image document. Components of an image document may include text, one or more paths, and one or more images. The text may be detected using optical character recognition (OCR) and the image document may be binarized. The detected text may be extracted from the binarized image document to enable detection of the paths, which may then be extracted from the binarized image document to enable detection of the images. In some examples, the images, similar to the text and paths, may be extracted from the binarized image document. The extracted text, paths, and/or images may be stored in a data store, and may be retrieved in order to create a flow document that may provide better adaption to a variety of reading experiences and provide editable documents.

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Belgrade, Republic of Serbia

Milan Sesum