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.

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 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.

Message composition and customization in a user's handwriting style includes obtaining electronic source text from a user, the electronic source text to be sent to a recipient, ascertaining properties of the electronic source text, the properties including words used in the electronic source text and a context of the electronic source text, and the context including an emotion of the electronic source text, and building an electronic message based on the ascertained properties, the electronic message including the electronic source text presented graphically in a handwriting style of the user.

Message composition and customization in a user's handwriting style includes obtaining electronic source text from a user, the electronic source text to be sent to a recipient, ascertaining properties of the electronic source text, the properties including words used in the electronic source text and a context of the electronic source text, and the context including an emotion of the electronic source text, and building an electronic message based on the ascertained properties, the electronic message including the electronic source text presented graphically in a handwriting style of the user.

A machine translation of a document is created via a compilation of services by mapping textual content from an image to create a plurality of mapped locations correspondent to at least one object from the image, populating each of the mapped locations with at least one character indicative of the object, each character sharing at least one similar attribute, adding to the image the populated mapped locations, and highlighting at least a portion of the textual content in accordance with the populated at least one character. A compilation of services is provided for identifying, extracting, and assessing electronic images by using a layered approach that reduces time and improves reviewing of medical records and other kinds of documentation.

A method for recognizing online handwriting comprising acquiring, by a handwriting instrument comprising a module comprising at least one motion sensor, motion data on the handwriting of the user when the user is writing a sequence of characters with the handwriting instrument, the handwriting instrument further including a body extending longitudinally between a first end and a second end, the first end having a writing tip which is able to write on a support, analyzing the motion data with a machine learning model trained in a multi-task way, the machine learning model being configured to deliver as an output the sequence of characters which was written by the user with the handwriting instrument.

A handwriting processing method, a handwriting processing device and a non-transitory storage medium. The handwriting processing method includes: acquiring a handwriting point group corresponding to a stroke on a working surface of a touch device, the handwriting point group including a plurality of handwriting points arranged in sequence, and data of each handwriting point in the plurality of handwriting points including a coordinate and an action type, determining a plurality of model patterns corresponding to the plurality of handwriting points of model patterns being in one-to-one correspondence with the plurality of handwriting points; and sequentially connecting the plurality of model patterns, to determine a handwriting track for displaying corresponding to the handwriting point group.

A handwritten content removing method and device and a storage medium. The handwritten content removing method comprises: acquiring an input image of a text page to be processed, the input image comprising a handwritten region, which comprises a handwritten content (S10); identifying the input image so as to determine the handwritten content in the handwritten region (S11); and removing the handwritten content in the input image so as to obtain an output image (S12).