The present disclosure relates to a computer-implemented method for handwriting-to-text-summarization, comprising obtaining, via a user interface of a system, a handwriting input representing a handwriting of a user of the system for handwriting-to-text-summarization, recognizing a text in the handwriting input, extracting at least one dynamic feature of the handwriting from the handwriting input, generating a text summary of the text, wherein generating the text summary is based on the text and on the at least one dynamic feature of the handwriting. The present disclosure also relates to a system for handwriting-to-text-summarization, comprising a user interface comprising a capturing subsystem configured to capture a handwriting of a user of the system, and wherein the system is configured to run the method for handwriting-to-text-summarization.

Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.

A device receives information indicating first names and last names of individuals and applies different cursive fonts to each of the first names and the last names to generate images of different cursive first names and different cursive last names. The device applies different transformations to the images of the different cursive first names and the different cursive last names to generate a set of first name images and a set of last name images. The device combines each first name image with each last name image to form a set of signature images and trains a neural network model, with the set of signature images, to generate a trained neural network model. The device receives an image of a signature and processes the image of the signature, with the trained neural network model, to recognize a first name and a last name in the signature.

An example image forming device includes an image input unit to receive a plurality of original documents, and a processor to extract handwriting from the respective original documents to generate a printed layer and a handwritten layer for each of the respective original documents, and to reconfigure the printed layers and a plurality of handwritten layers sharing the printed layers as respective files.

An example image forming device includes an image input unit to receive a plurality of original documents, and a processor to extract handwriting from the respective original documents to generate a printed layer and a handwritten layer for each of the respective original documents, and to reconfigure the printed layers and a plurality of handwritten layers sharing the printed layers as respective files.

A simplified handwriting recognition approach includes a first network comprising convolutional neural network comprising one or more convolutional layers and one or more max-pooling layers. The first network receives an input image of handwriting and outputs an embedding based thereon. A second network comprises a network of cascaded convolutional layers including one or more subnetworks configured to receive an embedding of a handwriting image and output one or more character predictions. The subnetworks are configured to downsample and flatten the embedding to a feature map and then a vector before passing the vector to a dense neural network for character prediction. Certain subnetworks are configured to concatenate an input embedding with an upsampled version of the feature map.

A simplified handwriting recognition approach includes a first network comprising convolutional neural network comprising one or more convolutional layers and one or more max-pooling layers. The first network receives an input image of handwriting and outputs an embedding based thereon. A second network comprises a network of cascaded convolutional layers including one or more subnetworks configured to receive an embedding of a handwriting image and output one or more character predictions. The subnetworks are configured to downsample and flatten the embedding to a feature map and then a vector before passing the vector to a dense neural network for character prediction. Certain subnetworks are configured to concatenate an input embedding with an upsampled version of the feature map.

Preserving ink effects in ink-to-text are described. A method of preserving styles and ink effects in ink-to-text can include receiving ink strokes and displaying the ink strokes on a canvas interface, each ink stroke comprising ink parameters such as pressure, ink color, and ink effect. In response to receiving a command to convert one or more ink strokes to text, the method can further include identifying text comprising characters and words from the one or more ink strokes; generating an appropriate coloring or style for each character or word based on the ink parameters associated with corresponding ink strokes, the appropriate coloring or style being generated based on a mapping between ink parameters and text parameters; applying the appropriate coloring or style to the text; and displaying the text on the canvas interface.

A method for generating word code, a method and device for recognizing codes are provided, and they are falling within the field of machine visual recognition. The method for recognizing word code includes: acquiring an image containing word codes, and recognizing target words therein; splitting the target words according to the same rule, and then recognizing all visual anomaly split sequences, and performing determination on the word codes, if the word codes are generated on the basis of an encoding mechanism designed by a system, directly reading same and performing invoking; otherwise, invoking a pre-inputted target file of the word codes according to target word meaning sequence numbers and visual anomaly sequences numbers corresponding to the word codes.

Techniques are provided for generating a digital image of simulated handwriting using an encoder-decoder neural network trained on images of natural handwriting samples. The simulated handwriting image can be generated based on a style of a handwriting sample and a variable length coded text input. The style represents visually distinctive characteristics of the handwriting sample, such as the shape, size, slope, and spacing of the letters, characters, or other markings in the handwriting sample. The resulting simulated handwriting image can include the text input rendered in the style of the handwriting sample. The distinctive visual appearance of the letters or words in the simulated handwriting image mimics the visual appearance of the letters or words in the handwriting sample image, whether the letters or words in the simulated handwriting image are the same as in the handwriting sample image or different from those in the handwriting sample image.