A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

Provided are a method for training a font generation model, a method for establishing a font library, and a device. The method for training a font generation model includes the following steps. A source-domain sample character is input into the font generation model to obtain a first target-domain generated character. The first target-domain generated character is input into a font recognition model to obtain the target adversarial loss of the font generation model. The model parameter of the font generation model is updated according to the target adversarial loss.

A computer-implemented method for handwritten text line wrapping includes: obtaining, from a user, at least two words of handwritten text on a screen; determining an original bounding box for the at least two words; creating at least one line-break character for the at least two words; determining at least one baseline for the at least two words; determining a new bounding box for the at least two words based on the at least one baseline; generating, on the screen, a text box; moving, on the screen, at least one of the at least two words from a first line of at least one line of handwritten text to a second line of the at least one line of handwritten text, wherein the second line of handwritten text fits within the text box; and adjusting at least one gap between the at least one line of handwritten text.

An image detection apparatus includes: a display outputting an image; a memory storing one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: detect, by using a neural network, an additional information area in a first image output on the display; obtain style information of the additional information area from the additional information area; and detect, in a second image output on the display, an additional information area having style information different from the style information by using a model that has learned an additional information area having new style information generated based on the style information.

A computer-implemented method for handwritten text line wrapping includes: obtaining, from a user, at least two words of handwritten text on a screen; determining an original bounding box for the at least two words; creating at least one line-break character for the at least two words; determining at least one baseline for the at least two words; determining a new bounding box for the at least two words based on the at least one baseline; generating, on the screen, a text box; moving, on the screen, at least one of the at least two words from a first line of at least one line of handwritten text to a second line of the at least one line of handwritten text, wherein the second line of handwritten text fits within the text box; and adjusting at least one gap between the at least one line of handwritten text.

In some implementations, an augmented reality (AR) device may receive first images representing a webpage, an email, a product, or a store associated with a first entity. The AR device may detect, within the first images, a logo, a font, and/or a color. The AR device may apply a model, trained on a set of guidelines associated with the first entity, to the logo, the font, and/or the color. Accordingly, the AR device may receive, from the model, a first score associated with the webpage, the email, the product, or the store. The AR device may transmit an alert based on the first score. In some implementations, the AR device may further receive second images and apply the model to receive a second score associated with the webpage, the email, the product, or the store. Accordingly, the AR device may transmit an additional alert based on the second score.

In some implementations, an augmented reality (AR) device may receive first images representing a webpage, an email, a product, or a store associated with a first entity. The AR device may detect, within the first images, a logo, a font, and/or a color. The AR device may apply a model, trained on a set of guidelines associated with the first entity, to the logo, the font, and/or the color. Accordingly, the AR device may receive, from the model, a first score associated with the webpage, the email, the product, or the store. The AR device may transmit an alert based on the first score. In some implementations, the AR device may further receive second images and apply the model to receive a second score associated with the webpage, the email, the product, or the store. Accordingly, the AR device may transmit an additional alert based on the second score.

Systems, devices and methods operative for identifying a reference within a figure and an identifier in a text associated with the figure, the reference referring to an element depicted in the figure, the reference corresponding to the identifier, the identifier identifying the element in the text, placing the identifier on the figure at a distance from the reference, the identifier visually associated with the reference upon the placing, the placing of the identifier on the figure is irrespective of the distance between the identifier and the reference.