Described are methods and systems for detecting fraud in documents. First images of a first set of genuine documents and second images of a second set of genuine documents are obtained. A printed feature, spacings between printed features in the first images, and positions of printed features in the second images are selected. Selected features, spacings and positions are annotated to obtain original landmark locations for each printed feature, spacing and position. Annotated features, spacings and positions are transformed to obtain transformed features, transformed spacings and transformed positions. The transformed features, spacings and positions are combined with a noise model to generate modified features, modified spacings and modified positions. Each modified feature, modified spacing and modified position comprises annotations indicating modified landmark locations. Input data for a machine learning model is generated using original landmark locations and modified landmark locations. The machine learning model is trained using the input data.

Described herein are methods, systems, and apparatuses for storage analysis and management using computer vision techniques. An imaging device may capture a series of images of a plurality of containers. The series of images may be analyzed by a classification model to determine a current capacity of each container. When the classification model determines that a current capacity of a container(s) satisfies a threshold(s), at least one remedial action may be performed.

Systems and methods are provided performing for low compute depth map generation by implementing acts of obtaining a stereo pair of images of a scene, downsampling the stereo pair of images, generating a depth map by stereo matching the downsampled stereo pair of images, and generating an upsampled depth map based on the depth map using an edge-preserving filter for obtaining at least some data of at least one image of the stereo pair of images.

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 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.

A system for file image processing and extraction of content from images is provided. The system comprises a computer and an application. When executed on the computer, the application receives a source document containing areas of interest and normalizes the document to align with a stored template image. The application also applies metadata associated with the template image to the areas of interest to identify data fields in the normalized document and extracts data from the identified data fields. The application also processes the extracted data using at least character recognition systems and produces a static structure using at least the identified data fields, the fields containing the processed data. The areas of interest comprise portions of the source document containing text needed to create and populate fields suggested by the stored template image. Normalizing the source document comprises at least one of flipping, rotating, expanding, and shrinking the document.

Unified framework for analysis and recognition of identity documents. In an embodiment, an image is received. A document is located in the image and an attempt is made to identify one or more of a plurality of templates that match the document. When template(s) that match the document are identified, for each of the template(s) and for each of one or more zones in the template, a sub-image of the zone is extracted from the image. For each extracted sub-image, one or more objects are extracted from the sub-image. For each extracted object, object recognition is performed. This may be done over one iteration (e.g., for a scanned image or photograph) or a plurality of iterations (e.g., for a video). Document recognition is performed based on the one or more templates and the results of the object recognition, and a final document-recognition result is output.

Computer-implemented methods and systems for curating virtual models and populating overlays within a virtual environment are described herein. A server may receive a data request from a user electronic device. The data request may comprise a property of interest located at a particular portion of an overall region. The server may then dynamically acquire a virtual model for rendering the property within a virtual environment at the user electronic device based on the data request. The server may then curate the virtual model in accordance with rules that emphasize features associated with the property that are relevant to assessing risks associated with the property when assessing the property. The server may then identify the curated property modeled by the virtual model, obtain annotation records associated with the features of the property, and populate an annotations overlay rendered in the virtual environment with information included in the annotation records.

A method for recognizing text includes: obtaining an image sequence feature of an image to be recognized; obtaining a full text string of the image to be recognized by decoding the image sequence feature; obtaining a text sequence feature by performing a semantic enhancement process on the full text string, in which the image sequence feature, the full text string and the text sequence feature are of the same length; and determining text content of the image to be recognized based on the full text string and the text sequence feature.

A method and system is provided for determining the denomination and related data for a currency item using a personal computing device, such as a mobile phone. The device includes or is connected to an image capture device that is preferably a digital video camera. At least one image of a target currency item is captured then processed for image quality. A further processing of the image includes a coordinate mapping. A comparison is made between individual pixels of the processed image based on the assigned coordinate mapping with a database of reference currency images to determine the currency denomination. Additional processing of the currency image provides the date and other data regarding the target currency item. A market value for the target currency item is identified by reference to a valuation database using the data determined for the currency item.