A method and a system are described for performing optical character recognition on an image including a plurality of printed characters. The method includes defining one or more opcodes and direction pointers associated with the plurality of printed characters of a language and a font type, wherein each of the one or more opcodes has an associated unique opcode characterization value. The method includes creating a binary tree comprising a plurality of nodes, wherein each node of the plurality of nodes is assigned the unique opcode characterization value. The method includes retrieving a set of operations associated with the unique opcode characterization value assigned to each of the plurality of nodes. The method includes navigating the binary tree from a root node to a leaf node based on the set of operations, the first pointer value, and the second pointer value until the leaf node is reached.

Systems, devices and methods operative for identifying a reference within a figure and an identifier in a text associated with the figure, the reference referring to an element depicted in the figure, the reference corresponding to the identifier, the identifier identifying the element in the text, placing the identifier on the figure at a distance from the reference, the identifier visually associated with the reference upon the placing, the placing of the identifier on the figure is irrespective of the distance between the identifier and the reference.

A method, computer system, and a computer program product for automatic equation transformation from text is provided. The present invention may include receiving a text document. The present invention may then include identifying a mathematical formula expressed in the received text document. The present invention may then include removing a plurality of superfluous language from the received text document based on the identified mathematical formula. The present invention may also include transforming the identified mathematical formula into a symbolic representation based on a trained model. The present invention may finally include outputting the symbolic representation.

An image processing device includes a storage device that previously stores a document image, a plurality of registered words, and a plurality of font characters, and a control device that functions as: a character region identifier that identifies a character region in the document image; an image acquirer that acquires an image of the character region; a text extractor that extracts a text from the image of the character region; a word identifier that identifies each of words in the text; a word determiner that determines whether each of the words is matched with one of the registered words; and a generator that generates a corrected text by replacing a target character of a non-matching word in the text with, among the font characters, a font character having a first degree of matching not lower than a first rate with the target character and a highest first degree of matching.

Disclosed is a method for automatically detecting an introduction/opening song within a multimedia file. The method includes designating sequential blocks of time in the multimedia file as scene(s) and detecting certain feature(s) associated with each scene. The extracted scene feature(s) may be analyzed and used to assign a probability to each scene that the scene is part of the introduction/opening song. The probabilities may be used to classify each scene as either correlating to or not correlating to, the introduction/opening song. The temporal location of the opening song may be saved as index data associated with the multimedia file.

A font switching method obtains, through pixel matching with a comparison font, a target font consistent with a font currently used by an operating system, and applies the target font to a third-party application, enabling the third-party application to accurately vary with a font change of the operating system and to avoid inconsistency between the font used by the third-party application and the font used by the operating system.

A method and apparatus of open set recognition, and a computer-readable storage medium are disclosed. The method comprises acquiring auxiliary data and training data of known categories for open set recognition, training a neural network alternately using the auxiliary data and the training data, until convergence; extracting a feature of data to be recognized for open set recognition, using the trained neural network; and recognizing a category of data to be recognized, based on the feature of the data to be recognized.

The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.

A medical image processing apparatus according to an embodiment comprises a memory and processing circuitry. The memory is configured to store a plurality of neural networks corresponding to a plurality of imaging target sites, respectively, the neural networks each including an input layer, an output layer, and an intermediate layer between the input layer and the output layer, and each generated through learning processing with multiple data sets acquired for the corresponding imaging target site. The processing circuitry is configured to process first data into second data using, among the neural networks, the neural network corresponding to the imaging target site for the first data, wherein the first data is input to the input layer and the second data is output from the output layer.