Disclosed is a display apparatus. The display apparatus includes a communication interface that receives an image from an external electronic device, a display that displays the image, and a processor, wherein the processor generates a user interface (UI) mask including probability information that a plurality of areas included in the image correspond to a UI, by using a convolutional neural network (CNN) algorithm, identifies a UI area included in the image by using the UI mask, identifies a text area included in the UI area, and recognizes text included in the text area.

A system for extraction and verification of handwritten signatures from arbitrary documents. The system comprises one or more computing devices configured to: receive a digital image of a document; perform a dilating transformation via convolution matrix on the digital image to obtain a modified image; determine a plurality of regions of connected markings in the digital image; based at least in part on a pixel density or proximity to an anchor substring of each region, determine whether any region contains any handwritten signature; extract first image data of the region containing a handwritten signature from the digital image; retrieve second image data of a confirmed example signature for a purported signer of the handwritten signature; and based on a comparison of the first image data with the second image data, forward a determination of whether the first image data and second image data are similar.

A method and an apparatus for generating an image are provided. The method includes: acquiring a screenshot of a webpage preloaded by a terminal as a source image; recognizing connection areas in the source image, and generating first circumscribed rectangular frames outside outlines of the connection areas; combining, in response to determining that a distance between the connection areas is smaller than a preset distance threshold, the connection areas, and generating a second circumscribed rectangular frame outside outlines of the combined connection areas; and generating, based on a nested relationship between the first circumscribed rectangular frames and the second circumscribed rectangular frames and pictures in the first circumscribed rectangular frames, a target image. The first circumscribed rectangular frames and the second circumscribed rectangular frame are respectively generated by recognizing and combining the connection areas in the source image.

A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200). The method includes: identifying components of a medical device by analyzing a technical document (130) related to the medical device; identifying component symbols (132) representing the components in drawings of the technical document; extracting relationships between the components of the medical device based on graphical connections (136) between the component symbols in the drawings of the technical document; generating a component connections graph (124) representing the relationships between the components of the medical device, the graph including nodes (138) corresponding to the components and connections (136) between the components; receiving an identification of a component to be replaced; and determining a co-replacement recommendation (122) for the component to be replaced based on the component connections graph.

A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200). The method includes: identifying components of a medical device by analyzing a technical document (130) related to the medical device; identifying component symbols (132) representing the components in drawings of the technical document; extracting relationships between the components of the medical device based on graphical connections (136) between the component symbols in the drawings of the technical document; generating a component connections graph (124) representing the relationships between the components of the medical device, the graph including nodes (138) corresponding to the components and connections (136) between the components; receiving an identification of a component to be replaced; and determining a co-replacement recommendation (122) for the component to be replaced based on the component connections graph.

Disclosed is a display apparatus. The display apparatus includes a communication interface that receives an image from an external electronic device, a display that displays the image, and a processor, wherein the processor generates a user interface (UI) mask including probability information that a plurality of areas included in the image correspond to a UI, by using a convolutional neural network (CNN) algorithm, identifies a UI area included in the image by using the UI mask, identifies a text area included in the UI area, and recognizes text included in the text area.

A system for extraction and verification of handwritten signatures from arbitrary documents. The system comprises one or more computing devices configured to: receive a digital image of a document; remove a subset of words from the digital image identified via OCR; determine a plurality of regions of connected markings that remain in the digital image; based at least in part on a pixel density or proximity to an anchor substring of each region, determine that a region contains a handwritten signature; extract first image data of the region containing a handwritten signature from the digital image; retrieve second image data of a confirmed example signature for a purported signer of the handwritten signature; and based on a comparison of the first image data with the second image data, forward a determination of whether the first image data and second image data are similar.

A method and an apparatus for generating an image are provided. The method includes: acquiring a screenshot of a webpage preloaded by a terminal as a source image; recognizing connection areas in the source image, and generating first circumscribed rectangular frames outside outlines of the connection areas; combining, in response to determining that a distance between the connection areas is smaller than a preset distance threshold, the connection areas, and generating a second circumscribed rectangular frame outside outlines of the combined connection areas; and generating, based on a nested relationship between the first circumscribed rectangular frames and the second circumscribed rectangular frames and pictures in the first circumscribed rectangular frames, a target image. The first circumscribed rectangular frames and the second circumscribed rectangular frame are respectively generated by recognizing and combining the connection areas in the source image.

Methods, apparatus, systems and articles of manufacture are disclosed for receipt decoding. An example apparatus for processing a receipt associated with a user disclosed herein includes an optical character recognition engine to generate bounding boxes, respective ones of the bounding boxes associated with groups of characters detected in the receipt, the bounding boxes including a first bounding box, a second bounding box and a third bounding box, a word connector to connect the first bounding box to the second bounding box based on (1) an adjacency of the first bounding box to the second bounding box and (2) a difference value from a comparison of a location of the first bounding box to a location of the second bounding box, a line connector to form a line of the ones of the bounding boxes by connecting the third bounding box to the second bounding based on a relationship between the first bounding box and the second bounding box, the line of the ones of the bounding boxes indicative of related receipt fields, and a creditor to generate a report based on the line.

An object is to provide a polygon detection device, a polygon detection method, and a polygon detection program to accurately detect a polygon resembling a reference polygon from an image.

The polygon detection device acquires a ratio among lengths of sides of a reference polygon included in an appearance of a predetermined object. The polygon detection device acquires a photographic image of the predetermined object. The polygon detection device detects line segments from the acquired photographic image. The polygon detection device forms at least one polygon based on the detected line segments. The polygon detection device identifies, from the formed polygon, a polygon corresponding to the reference polygon based on a degree of similarity between a ratio among lengths of sides of the formed polygon and the acquired ratio among the lengths of sides of the reference polygon, among from the formed polygon.