A computing system/method for enabling a user to improve, via training, a system designed to increase the emotional and/or physical well-being of persons, or designed for other purposes. The system/method includes retrieving a user response from a dialogue database, the user response having already labeled thereto an assigned class having a highest confidence score, the confidence score indicating degree of confidence that context of the retrieved user response is of the assigned class, displaying, the assigned class, along with other classes each having a respective lower confidence score, and receiving an indication of validity of the assigned class. The system/method further includes retrieving of a pair of sequential user response and follow-up prompt from the database, displaying user-selectable ratings, each rating designating a respectively different quality to the follow-up prompt, receiving selection of a rating and a related comment, and associating the selection and the comment to the follow-up prompt.

Example embodiments that analyze images to characterize aspects of the images rely on a same neural network to characterize multiple aspects in parallel. Because additional neural networks are not required for additional aspects, such an approach scales with increased aspects.

Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

A method and device for behavior control of a virtual image based on a text, and a medium are disclosed. The method includes inserting a symbol in a text, and generating a plurality of input vectors corresponding to the symbol and elements in the text; inputting the plurality of input vectors to a first encoder network, and determining a behavior trigger position in the text based on an attention vector of a network node corresponding to the symbol; determining behavior content based on a first encoded vector that is outputted from the first encoder network and corresponds to the symbol; and playing an audio corresponding to the text, and controlling the virtual image to present the behavior content when the audio is played to the behavior trigger position.

Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

Systems and methods for an augmented reality (AR)-assisted smart card for secure and accurate revision and/or submission of sensitive documents are provided. The methods may be executed via computer-executable instructions running on a microprocessor embedded in the smart card. A method may include capturing an image of a document, processing the image of the document, and computing, for one or more of the fields of the document, a recommended input. The method may further include comparing, for the one or more fields, the recommended input with an actual input, and, when the recommended input is more than a threshold difference apart from the actual input, generating a recommended revision. The method may also include displaying an AR image of the document on a display screen that is embedded in the smart card, said AR image comprising the image of the document augmented with the recommended revisions.

Techniques described herein take character glyphs as input and generate a text-on-a-path text object that includes the character glyphs arranged in a determined order along a path. For instance, a method described herein includes accessing character glyphs in input data. The method further includes determining an order for the character glyphs based on relative positions and orientations of the character glyphs in the input data. The method further includes generating a path for the character glyphs, based on the order, and associating the path with the character glyphs. Further, the method includes generating a text object that includes the set of character glyphs arranged in the order along the path.

Method and apparatus to match bounding boxes around text to align forms. The approach is less computationally intensive, and less prone to error than text recognition. For purposes of achieving alignment, information per se is not as important as information location. Information within the bounding boxes is not as critical as is the location of the area which the bounding boxes occupy. Scanning artifacts, missing characters, or noise generally do not affect bounding boxes themselves so much as they do the contents of the bounding boxes. Thus, for purposes of form alignment, the bounding boxes themselves are sufficient. Using bounding boxes also avoids misalignment issues that can result from stray marks on a page, for example, from holes punched in a sheet, or from handwritten notations.

Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

A system to extract data from regions of interest on a document is provided. The system includes a storage device storing an image derived from a document having text information. The system includes a document importer operable to perform optical character recognition to convert image data in the image to machine readable data. The system includes a neural network that identifies at least one region of interest on the image to classify an area of the at least one region of interest as a table. The neural network is operable to take as input the machine readable data and the image and combine both the machine readable data and the image to determine that the classified area is the table.