Systems of the present disclosure allow fields and labels to be identified in a digital image of a form without performing OCR. A digital image of a form can be partitioned into image segments using computer-vision image-segmentation techniques. Features for each image segment can be extracted using computer-vision feature-detection methods. The features extracted from an image segment can be included in an input instance for a machine-learning model. The machine-learning model can assign a classification to the input instance. The classification can associate the input instance with a field type or a label type.

Label and field identification without optical character recognition (OCR)

MOOC has brought many benefits to e-learning systems as students are able to obtain various educational presentation slides through digital libraries. These presentation slides provide varying levels of knowledge to specific students. On the other hand, students usually have different levels of knowledge. Thus, it is important to detect expertise levels of lecture slides for specific students, and supplement the lecture slides with related information automatically for different knowledge levels of students. Therefore, we developed a novel automatic slide reconstruction system for digital libraries in e-learning, it generates new lecture contents from one original content related to users’ interests and knowledge levels by adding and removing slides, in order to enable users to learn the reconstructed slides that they do not need no more searching. Our system first extracts topics and groups slides on topics to detect the expertise level of an original content by considering the context in the presentation. The system then searches other necessary contents and determines unnecessary original slide groups based on users’ interests and knowledge levels. Through this, the system can automatically reconstruct lecture slides by classifying them into four groups based on expertise of lecture slides. Those groups are: basic contents for beginners, basic or specialized contents for intermediate students, and specialized contents for advanced students. As a result, users can satisfy and joyfully learn the newly reconstructed slides that are suit to their interests and knowledge levels. In this paper, we discuss our automatic slide reconstruction system to deal with different knowledge levels of students for content understanding, knowledge deepening, and interest-expanding, and verify its effectiveness.

A Lecture Slide Reconstruction System Based on Expertise Extraction for e-Learning

Methods and systems for generating tailored user interface presentations based on skills clusters and automatically modified member profiles are presented According to various embodiments, a set of skills are accessed and a skills matrix generated A set of co-occurrences among the set of skills are identified A set of skills clusters is automatically generated based on identifying of the co-occurrences and the skills clusters are automatically validated A graphical representation of the validated skills cluster is presented with user interface elements for modifying the validated skills cluster and data representing member profiles is presented based on the validated skills cluster

Generating graphical presentations using skills clustering

A solution for formatting document objects by visual suggestions is proposed. For a document including multiple editing units each of which contains multiple objects, a first editing rule is generated, the first editing rule indicating an edit performed on a first object in a document (210). Then an editing suggestion is generated based on the first editing rule, the editing suggestion describing a candidate edit to be performed on a second object within a suggestion scope (220). The editing suggestion is then presented in a visual form to a user for selection (230).

Formatting document objects by visual suggestions

Exemplary embodiments allow contextual editing to proceed in real time while the editor views the actual web page (or other page) being edited. The editing may change the underlying content without changing the look and feel of the web page. Example embodiments enable an editor or web page developer to make changes to already published content by changing cached or locally stored content without changing the original content stored in databases.

Contextual editing in a page rendering system

Good alignment and repetition of objects across presentation slides can facilitate visual processing and contribute to audience understanding. However, creating and maintaining such consistency during slide design is difficult. To solve this problem, we present two complementary tools: (1) StyleSnap, which increases the alignment and repetition of objects by adaptively clustering object edge positions and allowing parallel editing of all objects snapped to the same spatial extent; and (2) FlashFormat, which infers the least-general generalization of editing examples and applies it throughout the selected range. In user studies of repetitive styling task performance, StyleSnap and FlashFormat were 4-5 times and 2-3 times faster respectively than conventional editing. Both use a mixed-initiative approach to improve the consistency of slide decks and generalize to any situations involving direct editing across disjoint visual spaces.

/pdf/mixed-initiative-approaches-to-global-editing-in-slideware-2damderqgf.pdf

Mixed-Initiative Approaches to Global Editing in Slideware

Techniques for selecting and editing visual elements (e.g., shapes) across multiple visuals (e.g., presentation slides) are described. The techniques obtain the multiple visuals each including visual elements. The visual elements may be grouped and synchronized based on similarities of an attribute among the visual elements. The visual elements may be presented to a user for evaluation. The user may select and make changes to a visual element. These changes may be propagated to other visual elements that belong to the same group of the visual element.

Selecting and editing visual elements with attribute groups

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Mixed-Initiative Approaches to Global Editing in Slideware

Selecting and editing visual elements with attribute groups