Position detection methods and systems are disclosed herein. The position detection method of detecting a position in an operation surface pointed by a pointing element includes obtaining a first taken image with the first infrared camera, obtaining a second taken image with the second infrared camera, removing a noise component from the first and second images converting the first and second taken into converted images without the noise component, forming a difference image between the first converted taken image and the second converted taken image, extracting a candidate area in which a disparity amount between the first converted taken image and the second converted taken image is within a predetermined range, detecting a tip position of the pointing element from the candidate area, and determining a pointing position of the pointing element and whether or not the pointing element had contact with the operation surface based on the detecting.

An image processing method and apparatus for a smart pen, and an electronic device are provided in embodiments of the present disclosure, and belong to the technical field of data processing. The method comprises: monitoring a working state of a second pressure switch provided at a pen tip of a smart pen after a first pressure switch of the smart pen is in a closed state; controlling an image collection module on the smart pen to collect a reflected infrared signal from an area where the smart pen writes; performing feature extraction processing on an original image to obtain a feature matrix corresponding to the original image; determining, based on current load status of the smart pen, the number of convolutional layers used for convolution processing in parallel convolutional layers; and adding current time information to a trajectory classification result to form a time-ordered trajectory vector.

An image processing method and apparatus for a smart pen, and an electronic device are provided in embodiments of the present disclosure, and belong to the technical field of data processing. The method comprises: monitoring a working state of a second pressure switch provided at a pen tip of a smart pen after a first pressure switch of the smart pen is in a closed state; controlling an image collection module on the smart pen to collect a reflected infrared signal from an area where the smart pen writes; performing feature extraction processing on an original image to obtain a feature matrix corresponding to the original image; determining, based on current load status of the smart pen, the number of convolutional layers used for convolution processing in parallel convolutional layers; and adding current time information to a trajectory classification result to form a time-ordered trajectory vector.

Techniques for an ink experience with images are discussed herein. In various implementations, an image is displayed via an image management application for viewing and/or editing images. In conjunction with interaction scenarios provided via the application, an inking mode for adding inked annotations to the image is enabled. Input to apply one or more inked annotations to the image is obtained, such as via finger touches on a touchscreen, drawing with a stylus, camera-based gestures, or other natural input mechanisms. Responsive to obtaining the input, data blocks corresponding to the one or more inked annotations are appended to an image file as additional data blocks for the image.

Described herein are systems, methods, and other techniques for training a generative adversarial network (GAN) to perform an image-to-image transformation for recognizing text. A pair of training images are provided to the GAN. The pair of training images include a training image containing a set of characters in handwritten form and a reference training image containing the set of characters in machine-recognizable form. The GAN includes a generator and a discriminator. The generated image is generated using the generator based on the training image. Update data is generated using the discriminator based on the generated image and the reference training image. The GAN is trained by modifying one or both of the generator and the discriminator using the update data.

A method includes obtaining input information defining a user input associated with a user of a first electronic device at a second electronic device. The method also includes presenting, on a display screen of the second electronic device, an avatar. The method further includes causing, using at least one processor of the second electronic device, the avatar on the display screen of the second electronic device to draw the user input on the display screen of the second electronic device. The avatar has associated dimensions within an avatar space, and a first draw path used by the avatar to draw the user input is normalized based on the dimensions of the avatar within the avatar space.

An information processing apparatus includes: a determiner that determines an area including a handwritten figure from image data; a recognizer that recognizes a handwritten character from the handwritten figure; an acquirer that acquires a file name; and a file generator that generates a file with a file name based on a handwritten character when the recognizer recognizes the handwritten character based on the image data and generates a file with the file name acquired by the acquirer when the recognizer does not recognize a handwritten character.

An information processing apparatus includes: a determiner that determines an area including a handwritten figure from image data; a recognizer that recognizes a handwritten character from the handwritten figure; an acquirer that acquires a file name; and a file generator that generates a file with a file name based on a handwritten character when the recognizer recognizes the handwritten character based on the image data and generates a file with the file name acquired by the acquirer when the recognizer does not recognize a handwritten character.

A method and system for inserting hand-written text is disclosed. The method includes detecting, from a stylus, an insertion gesture on a touch screen, determining, on the touch screen, an insertion location where the hand-written text is to be inserted, generating, on the touch screen, an insertion box for receiving the hand-written text from the stylus, detecting, from the stylus, the hand-written text in the insertion box, and, in response to determining that the hand-written text nears or exceeds a boundary of the insertion box, increasing a size of the insertion box to accommodate the hand-written text. The method further includes detecting, from the stylus, a completion gesture on the touch screen, reducing the size of the insertion box to encapsulate the inserted hand-written text, and erasing the insertion box and inserting the hand-written text into a space previously occupied by the insertion box.

An exemplary system is configured to detect user input directing a telestration element to be drawn within an image depicting a surface within a scene; render, based on depth data representative of a depth map for the scene and within a three dimensional (3D) image depicting the surface within the scene, the telestration element; record a 3D position within the scene at which the telestration element is rendered within the 3D image; detect a telestration termination event that removes the telestration element from being rendered within the 3D image; and indicate, subsequent to the telestration termination event, an option to again render the telestration element at the 3D position.