A character input method using a touch screen, in which one or more areas requiring user input is defined in the touch screen, pre-recognized information is defined for each of the defined areas, character information is received by a user in one or more user desired areas among the defined areas, the character information is recognized using a character recognizer, and the recognized character information is updated in the user desired areas.

A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to scan session data representative of operation of a user interface comprising a plurality of user interface elements; detect, at a point in the session data, at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; store an association between the error and the point in the session data; and provide access to the point in the session data via the association.

An image of a document is received from an image capture device, the image being in a format of an image file. At least one location of a user input field is automatically detected within the image based on patterns previously detected in a set of other images that were annotated to identify locations of user input fields within the individual images of the set. Coordinates are determined for the at least one location, and an electronic document is generated based on the received image. Generation of the electronic document includes addition of a software user input component at the location within the image with use of the coordinates, the software user input component configured to receive input from a user in electronic form.

Method and system of automatically extracting information of a predefined type from a document is provided. The method comprises using an object detection algorithm to identify at least one segment of the document that is likely to comprise the information of the predefined type. The method further comprises building at least one bounding box corresponding to the at least one segment and if the bounding box is likely to comprise the information of the predefined type extracting the information comprised by the bounding box from the at least one bounding box.

A character defect detection method and device are disclosed. The character defect detection method comprises: acquiring an image to be inspected containing a character and a template character image corresponding to the character, and converting the image to be inspected into an image having a format consistent with a format of the template character image; extracting a first valid area containing the character from the image to be inspected, and extracting a second valid area containing a template character from the template character image; obtaining a first image based on the first valid area, and obtaining a second image based on the second valid area; calculating a transformed image indicating an information difference between the first image and the second image based on the first image and the second image; and obtaining a character defect detection result of the image to be inspected based on information of the transformed image.

The present disclosure describes examples of a computer-implemented framework that helps to detect deception in charts and/or associated articles through textual and visual annotations.

A system may include a camera aimed at a display and configured to capture an image of the display, the display displaying a test image. The system may include a processor configured to: determine whether the display passes a verification test of correctly displaying a test image within a predetermined level of acceptance; and output a result of the verification test.

A computer system is provided. The computer system includes a memory and at least one processor coupled to the memory. The at least one processor is configured to scan session data representative of operation of a user interface comprising a plurality of user interface elements; detect, at a point in the session data, at least one changed element within the plurality of user interface elements; classify, in response to detecting the at least one changed element, the at least one changed element as either indicating or not indicating an error; store an association between the error and the point in the session data; and provide access to the point in the session data via the association.

The technology described herein generates a unique identifier for a visual media that comprises pre-printed visual indications on the visual media and a user's handwritten signature. The location of the signature on the visual media can be determined by including preprinted fiducial marks on the visual media. The fiducial markers act as landmarks that allow the size and location of the signature to be determined in absolute terms. The unique identifier is then stored in computer memory on a user-experience server. The user-experience server can associate the unique identifier with a digital asset, such as an image or video, designated by the user. When the unique identifier is provided to the user-experience server a second time, the digital asset can be retrieved and output to the computing device that provided the unique identifier.

Methods and systems are presented for extracting categorizable information from an image using a graph that models data within the image. Upon receiving an image, a data extraction system identifies characters in the image. The data extraction system then generates bounding boxes that enclose adjacent characters that are related to each other in the image. The data extraction system also creates connections between the bounding boxes based on locations of the bounding boxes. A graph is generated based on the bounding boxes and the connections such that the graph can accurately represent the data in the image. The graph is provided to a graph neural network that is configured to analyze the graph and produce an output. The data extraction system may categorize the data in the image based on the output.