A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

Symbol prediction can be implemented using a multi-task system trained for different tasks. The tasks may include a single symbol prediction, symbol category prediction, and symbol subcategory prediction. Categories of symbols can be generated by clustering sets of training data using a clustering scheme.

A document processing method includes receiving one or more documents, performing optical character recognition on the one or more documents to detect words comprising symbols in the one or more documents, and determining a encoding value for each of the symbols. It further includes applying a first hash function to each encoding value to generate a first set of hashed symbol values, applying a second hash function to each hashed symbol value to generate a vector array including a second set of hashed symbol values, and applying a linear transformation to each value of the second set of hashed symbol values of the vector array. The method also includes applying an irreversible non-linear activation function to the vector array to obtain abstract values associated with the symbols and saving the abstract values to train a neural network to detect fields in an input document.

The present disclosure relates to a method, an apparatus, a system, and a computer program for correcting table coordinate information. The present disclosure discloses a method for correcting table coordinate information performed by one or more processors in an apparatus, which may include: producing a list of first coordinate elements for a first axis or second coordinate elements for a second axis on the basis of coordinate information of a plurality of cells included in a table of a document image; performing grouping on the list of first coordinate elements or the list of second coordinate elements; and correcting the coordinate information of the cells of the table on the basis of the first coordinate elements or the second coordinate elements on which the grouping was performed.

A document processing method includes receiving one or more sets of documents, and assigning each document to one or more basic clusters based on the metadata of the document. It further includes for each cluster, training a respective basic cluster model detecting one or more visual element types, and responsive to a first threshold criterion measure related to the one or more basic clusters being satisfied, generating one or more superclusters based on an attribute shared by documents comprised by the plurality of basic clusters. The method also includes training a respective supercluster model detecting the one or more element types and generating a generalized cluster from the one or more superclusters. It includes training a generalized model for the generalized cluster, receiving an input document, assigning the input document to corresponding clusters, and detecting visual elements by processing the input document by each of the corresponding models.

A system 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 the accuracy of the characters by comparing to a secondary database.

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.

Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

A method includes: obtaining a first image including a target item; selecting a plurality of reference images corresponding to the first image from a database; performing word segmentation on item text information corresponding to the plurality of reference images to obtain a plurality of words; and extracting a key word meeting a reference condition from the plurality of words, and determining the extracted key word as an item name of the target item.