A user-selected group of data points is received. Weighted distances between further data points with the user-selected group of data points are computed, the weighted distances computed based on respective weights assigned to dimensions of data points. Density-based grouping of the further data points is performed based on the computed weighted distances, the density-based grouping producing cohorts of data points. A graphical visualization is generated including pixels representing the user-selected group of data points and the cohorts of data points. The graphical visualization provides a temporal-based visualized identification of the cohorts with the user selected group of data points.

A character recognizing apparatus includes an acquiring unit, an identifying unit, and a character recognizing unit. The acquiring unit acquires a string image that is an image of a string generated in accordance with one of multiple string generation schemes. The identifying unit identifies a range specified for a result of character recognition in each of the multiple string generation schemes. The character recognizing unit performs first character recognition on the string image, and if a result of the first character recognition has a feature of a particular string generation scheme of the multiple string generation schemes, the character recognizing unit performs second character recognition on the string image within the range specified for a result of character recognition in the particular string generation scheme.

Systems and methods for performing OCR of an image depicting text symbols and imaging a document having a plurality of planar regions are disclosed. An example method comprises: receiving a first image of a document having a plurality of planar regions and one or more second images of the document; identifying a plurality of coordinate transformations corresponding to each of the planar regions of the first image of the document; identifying, using the plurality of coordinate transformations, a cluster of symbol sequences of the text in the first image and in the one or more second images; and producing a resulting OCR text comprising a median symbol sequence for the cluster of symbol sequences.

An image-processing device includes: a reliability calculation unit configured to calculate reliability of a character recognition result for a document image which is a character recognition target on the basis of a descriptive feature amount of a character string of a specific item included in the document image; and an image output unit configured to output an image of the character recognition result indicating the character string of the specific item in a display mode in accordance with the reliability.

A computer-implemented method for processing a digital image. The digital image comprises one or more text cells, wherein each of the one or more text cells comprises a string and a bounding box. The method comprises receiving the digital image in a first format, the first format providing access to the strings and the bounding boxes of the one more text cells. The methods further comprises encoding the strings of the one or more text cells as visual pattern according to a predefined string encoding scheme and providing the digital image in a second format. The second format comprises the visual pattern of the strings of the one or more text cells. A corresponding system and a related computer program product is provided.

A system and method that enables users to provide authenticated medical records (e.g., vaccination records, viral anti-body test results, etc.) to a third-party (e.g., a venue) to gain access to the third-party is provided. In this way, the third party may confirm that the user is sufficiently immune to a particular disease (e.g., COVID-19) and may thereby minimize the threat of the user introducing the contagious disease to the third party. The system includes a biometric data recognition system that authenticates the identity of a user, a medical records acquisition system that acquires the medical records of the authenticated user, and a system for the displaying or otherwise providing the medical records to the third-party for review. The system also includes a system identification card that includes the user's contact information, alphanumeric characters associated with the user's driver's license number, medical records of the user, and other elements.

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.

An image-processing device includes: a character string detection unit configured to detect a character string of a specific item in a first document image based on a feature amount of the displayed first document image among feature amounts which are recorded in advance based on a result of learning using a plurality of document images and indicate features of the character string of the item for each kind of document image and each specific item; and an output unit configured to output information regarding a correspondence relation indicating the same specific item between the first document image and a second document image displayed to correspond to the first document image.

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.

Described techniques for multi-label classification, in which sequential data includes characters that have two or more aspects that require classification, are capable of providing separate classifications for different categories of components. Using an appropriately-trained neural network, the described techniques perform aligning and otherwise combining two or more classifications (e.g., categories, or types of labels) to obtain multi-label characters.