Provided is a notification management method of a mobile terminal including generating a screenshot image, determining a category of the screenshot image based on a text or an image included in the screenshot image, and extracting a text or an image related to the category and generating notification information using the extracted text or image. The user equipment and the AI system of this disclosure may be associated with artificial intelligence modules, drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, and devices related to 5G services.

Metadata for one or more highlights of a video stream may be extracted from one or more card images embedded in the video stream. The highlights may be segments of the video stream, such as a broadcast of a sporting event, that are of particular interest. According to one method, video frames of the video stream are stored. One or more information cards embedded in a decoded video frame may be detected by analyzing one or more predetermined video frame regions. Image segmentation, edge detection, and/or closed contour identification may then be performed on identified video frame region(s). Further processing may include obtaining a minimum rectangular perimeter area enclosing all remaining segments, which may then be further processed to determine precise boundaries of information card(s). The card image(s) may be analyzed to obtain metadata, which may be stored in association with at least one of the video frames.

Image analysis using visual geometry as an anchor for optical character recognition can be configured to receive an image acquired by a camera. The image is analyzed to detect a location within the image having a specified geometry. The specified geometry can be a predefined, visual geometry. The image is divided to create an image segment, where the image segment is based on the location of the specified geometry within the image. The image segment is analyzed to detect one or more characters within the image segment. The one or more characters in the image segment are decoded. A character string is generated based on decoding the one or more characters in the image segment.

Image analysis using visual geometry as an anchor for optical character recognition can be configured to receive an image acquired by a camera. The image is analyzed to detect a location within the image having a specified geometry. The specified geometry can be a predefined, visual geometry. The image is divided to create an image segment, where the image segment is based on the location of the specified geometry within the image. The image segment is analyzed to detect one or more characters within the image segment. The one or more characters in the image segment are decoded. A character string is generated based on decoding the one or more characters in the image segment.

An image processing system according to the present embodiment acquires a processing target image read from an original that is handwritten and specifies one or more handwritten areas included in the acquired processing target image. In addition, for each specified handwritten area, the present image processing system extracts from the processing target image a handwritten character image and a handwritten area image indicating an approximate shape of a handwritten character. Furthermore, for a handwritten area including a plurality of lines of handwriting among the specified one or more handwritten areas, a line boundary of handwritten characters is determined from a frequency of pixels indicating a handwritten area in a line direction of the handwritten area image, and a corresponding handwritten area is separated into each line.

An image processing system according to the present embodiment acquires a processing target image read from an original that is handwritten and specifies one or more handwritten areas included in the acquired processing target image. In addition, for each specified handwritten area, the present image processing system extracts from the processing target image a handwritten character image and a handwritten area image indicating an approximate shape of a handwritten character. Furthermore, for a handwritten area including a plurality of lines of handwriting among the specified one or more handwritten areas, a line boundary of handwritten characters is determined from a frequency of pixels indicating a handwritten area in a line direction of the handwritten area image, and a corresponding handwritten area is separated into each line.

A system, method, and computer program product provides a way to separate connected or adhered adjacent characters of a digital image for license plate recognition. As a threshold processing, the method performs a recognition of character adhesion by obtaining character parameters using an image processor. The parameters include a horizontal max crossing and a ratio of width and height. A first rule-based module is used responsive to the character parameters to distinguish the adhered characters (character adhesions) that are easy to judge, leaving the uncertain part to a character adhesion classifier model for discrimination. Character adhesion data is obtained by data augmentation including the adding of a random distance between two single characters to create class like adhered characters. Then the character adhesion classifier model of single character and character adhesion data is trained. Any uncertain part can be distinguished by the trained character adhesion classifier model.

In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

Multiple anchors may be utilized for robotic process automation (RPA) of a user interface (UI). The multiple anchors may be utilized to determine relationships between elements in the captured image of the UI for RPA. The results of the anchoring may be utilized for training or retraining of a machine learning (ML) component.