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: 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: 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: 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: 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.

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: 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.

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: 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.

Abstract: Detection of table of contents entries in a fixed format document for reconstruction of table of contents entries in a flow format document is provided. One or more table of contents entries are detected in a fixed format document, and table of contents entry candidates are generated by grouping one or more lines containing suspected table of contents entries. Each grouping is compared to text contained in the fixed format document for locating matching headings, subheadings, and associated text in the fixed format document. After non-matching or false positive matches are discarded, headings found in the fixed format document matching headings contained in table of contents entry candidates are used to reconstruct table of contents entries in a table of contents page, area or section in a reconstructed flow format document.

Abstract: 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.

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: A fixed format document conversion engine and associated method for converting a fixed format document into a flow format document. The fixed format document conversion engine includes a sequence of layout analysis engines and semantic analysis engines to analyzes the base physical layout information obtained from the fixed format document to enrich, modify, and classify the physical layout information into progressively more advanced physical layout information and, ultimately, semantic layout information. The semantic layout information is mapped and serialized into a selected flow format document with a high level of flowability.

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.