This application discloses a character recognition method and apparatus, an electronic device, and a medium. The character recognition method includes: obtaining a character picture, where the character picture includes at least one character; inputting the character picture to a grouped convolutional neural network model for prediction, to obtain character sequence prediction information corresponding to the character picture; and obtaining, based on the character sequence prediction information, a character recognition result corresponding to the character picture.

The present techniques relate to, inter alia, cryptographically-verifiable insurance credentials and cryptographically-verifiable property transfer. The novel methods and systems of decentralized identity discussed herein improve user experience (whether individual or organizational) by moving control over identity from the hands of centralized entities, back to where it belongsi.e., to the hands of individual organizations and users. In one aspect, a method includes obtaining a scanned image; processing the scanned image; transmitting a claim request; and receiving and storing an attestation response, and a computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: receive a claim request; cryptographically verify the claim; and transmit an attestation response.

An electronic device includes: a memory storing one or more instructions; when executed by at least one processor, cause the electronic device to: record an external voice for a certain period of time, recognize handwritten characters input based on an external input signal, generate handwritten text by converting the handwritten characters to text, determine whether there is a missing symbol based on the handwritten text, obtain, based on determining that there is the missing symbol, a voice segment from the recorded external voice recorded for the certain period of time, the voice segment corresponding to a time at which the missing symbol is input, generate target text by converting a voice included in the voice segment to text, determine recommended text corresponding to a location of the missing symbol based on the target text and the handwritten text, and change the missing symbol to the recommended text.

An apparatus and method. The apparatus including a least a processor configured to: receive a user profile from a profile database, identify a plurality of tasks using the user profile, determine at least an assignable task, receive internal personnel assignment data from a personnel database, determine internal personnel additional tasks, receive posting data, determine external personnel assignment data as a function of the posting data, generate a personnel list as a function of the internal personnel assignment data and the external personnel assignment data, generate at least one personnel assignment for the assignable task as a function of the personnel list; and transmit the at least a personnel assignment to a user device.

Receiving image data of a location in the form of a video feed, pictures, and/or depth information, as examples, from a client device controlled by a user, and/or other data is described. The received data serves as an input to a model (e.g., an artificial intelligence (AI)-based model such as a machine learning model) configured to generate an electronic representation of the location enriched with spatially localized details about surfaces and contents of the location. The electronic representation can be used for various purposes. The present disclosure provides a systems, methods, and computer programs that resolve several impediments in existing three dimensional image data acquisition and/or visualization systems by anonymizing personally identifying information (PII) in the received image data.

Techniques for logical text passage generation and retrieval for retrieval-augmented generation. The techniques involve processing markup language documents to generate logical text passages and their corresponding embeddings. These embeddings are indexed for efficient retrieval. Upon receiving a user utterance, a user query is formed and transformed into an embedding to query the index. Relevant text passages are identified and used to prompt a large language model (LLM), which generates a completion. This completion is then sent as a response to the user. The process effectively bridges user queries with relevant information through advanced embedding and natural language processing techniques, enabling accurate and contextually appropriate interactions within a user-agent dialogue framework.

An information processing system includes an imaging unit that generates an image signal by imaging and an information processing device. The information processing device performs at least any one of plural kinds of image processing on a taken image corresponding to the image signal. The information processing device specifies an object corresponding to a partial image included in the taken image on the basis of a state of the object corresponding to the partial image included in the taken image or a degree of reliability given to a processing result of the performed image processing.

Disclosed are a wine product positioning method, a wine product information management method and apparatus, a computer device, and a computer-readable storage medium. Based on a preset camera in a wine cellar, a wine product image captured by the preset camera and corresponding to a target wine product is acquired (S21). Based on a preset wine label recognition method combining optical character recognition (OCR) and deep learning recognition, the wine product image is recognized to obtain a wine label corresponding to the wine product image (S22). A preset capture position corresponding to the camera is acquired, and the preset capture position is taken as a current position corresponding to the target wine product (S23). A position corresponding to the target wine product is described by using the wine label and the current position, to position the target wine product (S24).

Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.