The present application discloses a method for image text recognition, an apparatus, a device, and a storage medium, and relates to image processing technologies in the field of cloud computing. A specific implementation is: acquiring an image to be processed, where at least one text line exists in the image to be processed; processing each text line in the image to be processed to obtain a composite encoded vector corresponding to each word in each text line, where the composite encoded vector carries semantic information and position information; and determining a text recognition result of the image to be processed according to the semantic information and the position information carried in the composite encoded vector corresponding to each word in each text line. This technical solution can accurately distinguish adjacent fields with small pixel spacing in the image and improve the accuracy of text recognition in the image.

A text recognition method includes: acquiring an image including text information, the text information including M characters, M being a positive integer greater than 1; performing text recognition on the image to acquire character information about the M characters; recognizing reading direction information about each character in accordance with the character information about the M characters, the reading direction information being used to indicate a next character corresponding to a current character in a semantic reading order; and ranking the M characters in accordance with the reading direction information about the M characters to acquire a text recognition result of the text information.

A calligraphy-painting device, a calligraphy-painting apparatus, and an auxiliary method for calligraphy-painting are disclosed. The calligraphy-painting device includes: a display portion, configured to display a preset calligraphy-painting information; an image acquiring portion, configured to acquire an image; and a control unit, in communication connection with the display portion, and configured to control the display portion to display the preset calligraphy-painting information, wherein the image is processed to obtain a calligraphy-painting region, and the preset calligraphy-painting information is virtually displayed in the calligraphy-painting region.

A method, an apparatus, a device and a computer-readable storage medium for recognizing aerial handwriting are provided. The method may include detecting a palm region of a user in a two-dimensional grayscale image; detecting a fingertip in the two-dimensional gray-scale image based on the palm area; determining a spatial trajectory of the fingertip based on a set of two-dimensional gray-scale images, the set of two-dimensional gray-scale images including the two-dimensional gray-scale image; and recognizing handwritten content of the user based on the spatial trajectory. A two-dimensional gray-scale image is used to recognize and track the spatial trajectory of the fingertip, which may speed up aerial handwriting recognition, and has low processing performance requirements for the device, while also ensuring high accuracy.

The present invention discloses a method and a device for evaluating and sorting of trademark query results similarities. The method comprises: performing scorecard processing on sample trademarks and input trademarks from different perspectives like shape, sound and meaning, so as to respectively acquire shape, sound and meaning scorecard information of the sample trademark and the input trademarks; acquiring matching information between the resultant trademarks and the input trademarks by retrieving, and then calculating a shape similarity, a trademark meaning similarity, a trademark sound similarity and a scoring rate of retrieval keywork matching between the resultant trademarks and the input trademarks according to preset formulas, thereby acquiring comprehensive quantified values of trademark similarities by calculation, and sorting the resultant trademarks according to magnitudes of the comprehensive quantified values of trademark similarities.

According to one embodiment, an information processing device includes a storage device and a processor connected to the storage device. The processor is configured to perform a process for displaying an image and information related to a size of a recognition target, and recognize a recognition target from the image. The information related to the size of the recognition target includes visual information of the recognition target.

Systems and methods to receive one or more first images associated with a training set of images to train a machine learning model; provide the one or more first images as a first input to a first set of layers of computational units, wherein the first set of layers utilizes image filters; provide a first output of the first set of layers of computational units as a second input to a second layer of the computational units, wherein the second layer utilizes random parameter sets for computations; obtain distortion parameters from the second layer of the computational units; generate one or more second images comprising a representation of the one or more first images modified with the distortion parameters; obtain, as a third output, the one or more second images; and add the one or more second images to the training set of images to train the machine learning model.

An electronic device and method are disclosed herein. The electronic device includes: a processor, and a memory operatively connected to the processor. The processor implements the method, including: receive an input of handwritten text including characters, set corpus line key points indicating a substantial-top positions of the characters included in the handwritten text and base line key points indicating a substantial bottom positions of the characters, calculate at least a first feature value based on at least one corpus line key point from among the corpus line key points, and a second feature value based on at least one extracted base line key point from among the base line key points, respectively, input the calculated first feature value into a first neural network to cause the first neural network to generate a first result value, input the calculated second feature value into a second neural network to cause the second neural network to generate a second result value, and input the first result value and the second result value into a fully connected neural network to generate a third result value.

Implementations for optical character recognition of a language script can include actions of receiving an image comprising a graphical representation of a word written in the language script, segmenting the word into two or more segments, each segment being determined based on one or more of a variation in a height of the word and a variation in a width of the word, and including at least one character, providing a boundary for each segment of the two or more segments, the boundary enclosing the at least one character of a respective segment, each boundary having an edge with respect to an axis of the image, normalizing boundaries of the two or more segments by aligning edges of the boundaries, and labeling each segment of the two or more segments with a respective label, the respective label indicating a language character within the respective boundary.

One or more embodiments of systems and methods for a digital content management system for creating a digital document from handwritten content are described herein. For example, the digital content management system receives a digital image of handwritten content and analyzes the digital image to identify handwritten content as well as to identify specific command indicators. In response to identifying a command indicator associated with a command to create a digital document, the digital content management system creates a new digital document and adds digital content portions to the digital document that correspond to the identified content portions identified within the handwritten content depicted within the digital image.