Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, facilitate cross-platform content muting. Methods include detecting a request from a user to remove, from a user interface, a media item that is provided by a first content source and presented on a first platform. One or more tags that represent the media item are determined. These tags, which indicate that the user removed the media item represented by the one or more tags from presentation on the first platform, are stored in a storage device. Subsequently, content provided by a second content source (different from the first content source) on a second platform (different from the first platform) is prevented from being presented. This content is prevented from being presented based on a tag representing the content matching the one or more tags stored in the storage device.

A content capturing system is suitable for capturing content in an image of a document. The content capturing system includes a processor and a storage device. The processor accesses the program stored in the storage device to implement a cutting module and a processing module. The cutting module receives a corrected image. The content in the corrected image includes a plurality of text areas, and the cutting module inputs the corrected image or a first text area into a convolutional neural network. The convolutional neural network outputs the coordinates of the first text area. The cutting module cuts the first text area according to the coordinates of the first text area. The cutting module inputs the cut first text area into a text recognition system and obtains a plurality of first characters in the first text area from the text recognition system.

In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.

Multiple anchors may be utilized for robotic process automation (RPA) of a user interface (UI). The multiple anchors may be utilized to determine relationships between elements in the captured image of the Ul for RPA. The results of the anchoring may be utilized for training or retraining of a machine learning (ML) component.

A computing system may be configured to determine payment details for funds transfers from unstructured sets of data. The system may maintain historical transaction information associated with each of a plurality of users. The system may receive data associated with a transaction. The system may identify a user associated with the data, wherein the user is one of the plurality of users. The system may determine, based on contextual information of the data and the historical transaction information associated with the user, payment details about the transaction. The payment details may include a payment amount and one or more recipients. The system may execute a funds transfer for the payment amount from a source account associated with the user to one or more destination accounts associated with the one or more recipients.

A content capturing system is suitable for capturing content in an image of a document. The content capturing system includes a processor and a storage device. The processor accesses the program stored in the storage device to implement a cutting module and a processing module. The cutting module receives a corrected image. The content in the corrected image includes a plurality of text areas, and the cutting module inputs the corrected image or a first text area into a convolutional neural network. The convolutional neural network outputs the coordinates of the first text area. The cutting module cuts the first text area according to the coordinates of the first text area. The cutting module inputs the cut first text area into a text recognition system and obtains a plurality of first characters in the first text area from the text recognition system.

The method, system, and non-transitory computer-readable medium embodiments described herein provide for identifying invalid identification documents. In various embodiments, an application executing on a user device prompts the user device to transmit an image of the identification document. The application receives an image including the identification document in response to the identification document being within a field of view of a camera of the user device. The identification document includes a plurality of visual elements, and one or more visual elements of the plurality of visual elements are one or more invalidating marks. The application detects a predetermined pattern on the identification document in the image, the predetermined pattern formed from the one or more invalidating marks. The application determines that the identification document is invalid based on the detected predetermined pattern.

The disclosed embodiments describe methods, systems, and apparatuses for determining user intent. A method is disclosed comprising obtaining a session text of a user; calculating, by the processor, a feature vector based on the session text; determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning a user intent to the session text based on the probabilities.

It is an object of the present invention to provide an object collating device and an object collating method that enable matching of images of a dividable medical article with desirable accuracy and easy confirmation of matching results. In the object collating device according to the first aspect, when the object is determined to be divided, the first image for matching is collated with the image for matching (the second matching image) for the objects in the undivided state, so that the region to be matched is not narrowed, and matching of the images of the dividable medical article is achieved with desirable accuracy. In addition, since the first and second display processing is performed on the images for display determined to contain the objects of the same type, matching results can easily be confirmed.