Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Questions about a passage of text that includes a sequence of two or more sentences are generated. Each question covers the content of a plurality of sentences in the passage, and includes a context portion of the passage and a question statement that is contextually related to the context portion of the passage. A user is also provided with questions about a passage of text they are reading. Each question is presented to the user, where this presentation includes displaying the context portion of the passage and the question statement that is contextually related to the context portion of the passage.

Abstract: Various embodiments provide techniques for adaptive content layout. In at least some embodiments, templates are provided that can be utilized to adapt content for a particular device and/or display configuration. A template can be embodied in a variety of different ways, such as XML code (or other markup code), script code, a component object model (COM) object, and so on. Examples of content that can be adapted using a template include video content, images, web pages, and so on. According to some embodiments, each template can include one or multiple elements that can be populated with content. An element can have a particular size layout, such as a width and height (e.g., in pixels) and content can be reformatted and/or resized based on attributes (e.g., size and/or shape) of the element.

Abstract: Computer-storage media, computerized methods and systems for formatting a page layout according to form factors of a display area are provided. Initially, search results are received and dynamically converted into respective block-level elements, where each of the block-level elements is associated with a portion of content from one of the received search results. Form factors are retrieved from a graphical user interface. Typically the form factors are associated with properties of a display area initiated to present the search results. At least one adaptive template is selected according to the retrieved form factors. The portion of content of each of the block-level elements is formatted based on at least one adaptive template. Also, the adaptive template is utilized to establish content panes within the page layout, as directed by the form factors. These content panes are populated with one or more of the block-level elements.

Abstract: Computer-storage media, computerized methods and systems for formatting a page layout according to form factors of a display area are provided. Initially, search results are received and dynamically converted into respective block-level elements, where each of the block-level elements is associated with a portion of content from one of the received search results. Form factors are retrieved from a graphical user interface. Typically the form factors are associated with properties of a display area initiated to present the search results. At least one adaptive template is selected according to the retrieved form factors. The portion of content of each of the block-level elements is formatted based on at least one adaptive template. Also, the adaptive template is utilized to establish content panes within the page layout, as directed by the form factors. These content panes are populated with one or more of the block-level elements.

Abstract: Computer-storage media, computerized methods and systems for formatting a page layout according to form factors of a display area are provided. Initially, search results are received and dynamically converted into respective block-level elements, where each of the block-level elements is associated with a portion of content from one of the received search results. Form factors are retrieved from a graphical user interface. Typically the form factors are associated with properties of a display area initiated to present the search results. At least one adaptive template is selected according to the retrieved form factors. The portion of content of each of the block-level elements is formatted based on at least one adaptive template. Also, the adaptive template is utilized to establish content panes within the page layout, as directed by the form factors. These content panes are populated with one or more of the block-level elements.

Abstract: Computer-storage media, computerized methods and systems for formatting a page layout according to form factors of a display area are provided. Initially, search results are received and dynamically converted into respective block-level elements, where each of the block-level elements is associated with a portion of content from one of the received search results. Form factors are retrieved from a graphical user interface. Typically the form factors are associated with properties of a display area initiated to present the search results. At least one adaptive template is selected according to the retrieved form factors. The portion of content of each of the block-level elements is formatted based on at least one adaptive template. Also, the adaptive template is utilized to establish content panes within the page layout, as directed by the form factors. These content panes are populated with one or more of the block-level elements.

Abstract: A global optimization framework for optical character recognition (OCR) of low-resolution photographed documents that combines a binarization-type process, segmentation, and recognition into a single process. The framework includes a machine learning approach trained on a large amount of data. A convolutional neural network can be employed to compute a classification function at multiple positions and take grey-level input which eliminates binarization. The framework utilizes preprocessing, layout analysis, character recognition, and word recognition to output high recognition rates. The framework also employs dynamic programming and language models to arrive at the desired output.

Abstract: Techniques for modifying network content layout to be presented by an application are disclosed. More specifically, the content layout or portions thereof can be altered to facilitate specific tasks including, but not limited to, enhancing readability. For example, content can be laid out in one or more discrete areas and navigation enabled in unique sequential and/or non-sequential manners. Further yet, content can be modified, moved and/or removed, among other things. This functionality can be executed automatically or upon specific instruction, for instance as specified by a user, content provider or third party service.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Abstract: Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.