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: 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: The present disclosure is directed to a method for optically recognizing a table and converting that recognized table to a digitized format. In particular, the present disclosure relates to a method of optically recognizing and identifying a table generally, individual cells within the table, the data embedded within each cell, as well as the original table format, including shading, cell borders, colors, and effects. Accordingly, such digitization of an optically recognized table, in whole or in part, as printed on a document or other media allows users to easily and quickly capture information as originally arranged without having to manually re-create a table and enter data into the re-created table.

Abstract: 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.

Abstract: Technologies are described herein for generating structured sample authoring content based on a user input. Generally described, sample content, such as a synopsis of a subject, may be generated from a contextual interpretation of one or more keywords provided by a user. Using the one or more keywords, a system retrieves content data from one or more resources. The content data is parsed and used to generate a structure of the content data. The structure is then used to generate sample content that may be presented to the user. The presented information may provide a way to jumpstart an authoring project on particular topics of interest.

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: 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.