Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Document heading detection includes performing a classification on each of a plurality of paragraphs of a document to identify each paragraph as either a heading or non-heading paragraph. The classification is based on one or more pre-established values corresponding to one or more pre-established formatting features that are indicative of a heading paragraph relative to currently established values for each of the one or more pre-established formatting features in each of the plurality of paragraphs. Document heading detection further includes determining a strength of each of the one or more heading paragraphs by performing a linear regression on each heading paragraph and assigning each of the one or more heading paragraphs a heading level within a hierarchy of heading levels based on the determined strength.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Footnote detection is provided. When converting a fixed format document to a flow format document, footnotes, which may include a footnote description and a footnote reference mark, may be detected and linked with a corresponding footnote reference mark in a main document flow on a page. Additionally, a footnote numbering scheme may be determined and applied to detected footnotes. By providing footnote detection when converting a fixed format document to a flow format document, footnotes may be statically positioned at the end of a page containing the corresponding footnote reference mark.

Abstract: 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.

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 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.

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.

Abstract: 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.

Abstract: 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.