Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Abstract: There is a need for more effective and efficient predictive anomaly detection. This need can be addressed by, for example, solutions for performing anomaly detection using an anomaly detection machine learning model. In one example, a method includes: identifying a plurality of event records; for each event record of the plurality of event records, determining a temporally-related event code data object based at least in part on a temporally-related subset of the one or more event codes that is associated with the event record; generating one or more event record profiles based on each temporally-related event code data object; processing the one or more event record profiles using an anomaly detection machine learning model to generate one or more anomaly detection predictions; and performing one or more prediction-based actions based at least in part on the one or more anomaly detections.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining data describing one or more customer contacts of a user from a third-party content provider; matching the one or more customer contacts to respective additional third-party content associated with the one or more customer contacts; deriving geographic location information for one or more of the customer contacts based on the additional third-party content; receiving a geographic location of the user; determining one or more contact opportunities based on the geographic location of the user, the geographic location information for the one or more customer contacts, and one or more presentation criteria; and providing data describing the one or more contact opportunities to a user device for presentation, wherein the data is presented on a user interface of the user device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a user login request originating from a user device, the user login request including a user identifier; authenticating, based on the user identifier, the user as having access to information associated with a particular tenant of a plurality of tenants; identifying a tenant specified theme associated with the particular tenant of a plurality of tenant themes, the theme being specified by the tenant for use in presenting data to authenticated users; obtaining data associated with the particular tenant; and providing the data associated with the particular tenant to the user device for presentation, wherein the data is presented on a user interface of the user device, the data being formatted based on the identified tenant theme.