A video processing method and related devices are provided. The method includes: acquiring board writing images of consecutive frames from a video with unobstructed board writing; calculating a difference area between the image of a current frame and a reference image; replacing a corresponding image block in the board writing image of the current frame with a corresponding image block in the reference image to obtain a new board writing image for each of the difference area; outputting the new board writing image to form a new video. The board writing image of the current frame are sequentially each board writing image after the board writing image of a first frame in the board writing images of the consecutive frames, and the reference image is the board writing image associated with a previous board writing image.

A system and computer readable storage medium for automated handwriting generation, including a text input device for inputting a text query having at least one textual word string, an image input device for inputting a handwriting sample with characters in a writing style of a user, and a computer implemented deep learning transformer model including an encoder network and a decoder network in which each are a hybrid of convolution and multi-head self-attention networks. The encoder produces a sequence of style feature embeddings from the input handwriting sample. The decoder takes the sequence of style feature embeddings in order to convert the at least one textual word string into a generated handwritten image having substantially same writing style as the handwriting sample. An output device to output the generated handwriting image.

A system and computer readable storage medium for automated handwriting generation, including a text input device for inputting a text query having at least one textual word string, an image input device for inputting a handwriting sample with characters in a writing style of a user, and a computer implemented deep learning transformer model including an encoder network and a decoder network in which each are a hybrid of convolution and multi-head self-attention networks. The encoder produces a sequence of style feature embeddings from the input handwriting sample. The decoder takes the sequence of style feature embeddings in order to convert the at least one textual word string into a generated handwritten image having substantially same writing style as the handwriting sample. An output device to output the generated handwriting image.

A display apparatus includes circuitry that displays, on a display, shape data; receives stroke data input to a user interface with an input device; determines whether the stroke data intersects with the shape data; based on a determination that the intersection is detected, converts the stroke data and the shape data, being collectively recognized, into converted shape data; and displays, on the display, the converted shape data in place of the shape data.

A display apparatus includes circuitry that displays, on a display, shape data; receives stroke data input to a user interface with an input device; determines whether the stroke data intersects with the shape data; based on a determination that the intersection is detected, converts the stroke data and the shape data, being collectively recognized, into converted shape data; and displays, on the display, the converted shape data in place of the shape data.

A display apparatus includes circuitry to receive input of a hand drafted input, convert the hand drafted input into a shape, and determine whether the shape is an object selecting shape that is preset to be available for selecting an object displayed in the object selecting shape on a display. In a case that the shape is determined to be the object selecting shape, the circuitry displays, on the display, a display component to be operated for receiving selection of the object. In a case that the display component is operated, the circuitry causes the object to be a selected state.

A display apparatus includes circuitry to receive input of a hand drafted input, convert the hand drafted input into a shape, and determine whether the shape is an object selecting shape that is preset to be available for selecting an object displayed in the object selecting shape on a display. In a case that the shape is determined to be the object selecting shape, the circuitry displays, on the display, a display component to be operated for receiving selection of the object. In a case that the display component is operated, the circuitry causes the object to be a selected state.

A system and computer readable storage medium for automated handwriting generation, including a text input device for inputting a text query having at least one textual word string, an image input device for inputting a handwriting sample with characters in a writing style of a user, and a computer implemented deep learning transformer model including an encoder network and a decoder network in which each are a hybrid of convolution and multi-head self-attention networks. The encoder produces a sequence of style feature embeddings from the input handwriting sample. The decoder takes the sequence of style feature embeddings in order to convert the at least one textual word string into a generated handwritten image having substantially same writing style as the handwriting sample. An output device to output the generated handwriting image.

A system and computer readable storage medium for automated handwriting generation, including a text input device for inputting a text query having at least one textual word string, an image input device for inputting a handwriting sample with characters in a writing style of a user, and a computer implemented deep learning transformer model including an encoder network and a decoder network in which each are a hybrid of convolution and multi-head self-attention networks. The encoder produces a sequence of style feature embeddings from the input handwriting sample. The decoder takes the sequence of style feature embeddings in order to convert the at least one textual word string into a generated handwritten image having substantially same writing style as the handwriting sample. An output device to output the generated handwriting image.

A system and method for identifying handwritten characters on an image using a classification model that employs a neural network. The system includes a computer having a processor and a memory device that stores data and executable code that, when executed, causes the processor to read and convert typed text on the image to machine encoded text to identify locations of the typed text on the image; identify a location on the image that includes handwritten text based on the location of predetermined typed text on the image; identify clusters of non-white pixels in the image at the location having the handwritten text; generate an individual and separate cluster image for each identified cluster; classify each cluster image using machine learning and at least one neural network to determine the likelihood that the cluster is a certain character; and determine what character each cluster image is based on the classification.