Embodiments are disclosed for reflowing an infographic image for display in a mobile device using machine learning models. In particular, in one or more embodiments, the method may include receiving a document for display in a user device, the document including an infographic image. The method may further include identifying, using a convolutional neural network, visual components of the infographic image. The method may further include determining, using an encoder-decoder network, an ordered sequence of the identified visual components. A generative adversarial network then generates a modified visual representation of the infographic image based on the identified visual components and the determined ordered sequence of the identified visual components. The modified visual of representation of the infographic image is then presented for display in a viewing pane of a user device in place of the infographic image.

A computer-implemented method includes receiving a digital image of a document and a workflow describing an automation task. The method also include converting the digital image of the document into rich text that includes layout information in the document. The method further includes creating, based on the rich text and the workflow, a tree of thoughts that includes nodes and edges connecting the nodes and that binds at least some nodes representing the rich text with a task node representing the automation task. The method also includes converting the nodes and edges of the tree of thoughts into a natural language text. The method further includes inputting the natural language text into a language machine learning model with attention given to a token in the natural language text representing the task node. The language machine learning model, in response, outputs a result of completing the automation task.

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.

Methods, systems, and media for generating video classifications using multimodal video analysis are provided. In some embodiments, a method for classifying videos comprising: receiving, from a computing device, a video identifier; parsing a video associated with the video identifier into an audio portion and a plurality of image frames; analyzing the plurality of images frames associated with the video using (i) an optical character recognition technique to obtain first textual information corresponding to text appearing in at least one of the plurality of image frames and (ii) an image classifier to obtain, for each of a plurality of objects appearing in at least one of the plurality of frames of the video, a probability that an object appearing in at least one of the plurality of images falls within an image class; concurrently with analyzing the plurality of image frames associated with the video, analyzing the audio portion of the video using an automated speech recognition technique to obtain second textual information corresponding to words spoken in the video; combining the first textual information, the probability of each of the plurality of objects appearing in the at least one of the plurality of frames of the video, and the second textual information to obtain a combined analysis output for the video; determining, using a neural network, a safety score for each of a plurality of categories that the video contains content belonging to a category of the plurality of categories, wherein the combined analysis output is input into the neural network; and, in response to receiving the video identifier, transmitting a plurality of safety scores corresponding to the plurality of categories to the computing device for the video associated with the video identifier.

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.

Provided is a system for implementing an artificial intelligence (AI) model for extracting meta information and data information included in a chart. The system includes at least one processor; and at least one memory storing instructions for the processor. The processor is configured to input the chart into an image encoder to convert the chart into a first embedding processable by the AI model, input the first embedding to the AI model to output a second embedding including the meta information from the first embedding, and to output a fourth embedding including the data information from a third embedding including information about an entity included in the second embedding, and output each of a first data format in which the meta information included in the second embedding is recorded, and a second data format in which the data information included in the fourth embedding is recorded.

The present disclosure provides a method of facilitating an automated asset transaction. Further, the method may include receiving an asset transaction data from a user device associated with a user. Further, the asset transaction data corresponds to a transaction associated with an asset. Further, the method may include processing the asset transaction data. Further, the method may include identifying a transaction characteristic data based on the processing. Further, the transaction characteristic data corresponds to a characteristic associated with the transaction. Further, the method may include storing the transaction characteristic data. Further, the method may include generating a transaction update data based on the transaction characteristic data. Further, the transaction update data corresponds to an update in relation to the transaction. Further, the generating may be further based on an AI module. Further, the method may include transmitting the transaction update data to the client device.

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.

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

In one general aspect, a method can include capturing, using an image sensor, a first raw image at a first resolution, converting the first raw image to a digitally processed image using an image signal processor, and analyzing at least a portion of the digitally processed image based on a processing condition. The method can include determining that the first resolution does not satisfy the processing condition; and triggering capture of a second raw image at the image sensor at a second resolution greater than the first resolution.