An out-of-stock detection system notifies store management that a product is out of stock. The system captures images of a shelf and determines the position product labels thereon. For each product label, a bounding box is generated based on the position of each product label on the shelf. The system then identifies a product for each product label based on information within each product label and, for each product label, stores a product identified for each bounding box. Accordingly, the system performs an out-of-stock detection process that includes capturing additional image data of the shelf periodically that includes each bounding box, providing a portion of the additional image data for each bounding box to a model trained to determine whether the bounding box contains products, sending a notification for a product determined to be out of stock to a store client device based on output from the model.

The present invention discloses a method, a system and a computer program product for data processing and classification. The invention provides warm start and cold start classification tools for classification of data obtained from known or unknown entities. The system and method are also configured to be employed over blockchain based networks.

A processor may receive an image and identify a plurality of characters in the image using a machine learning (ML) model. The processor may generate at least one word-level bounding box indicating one or more words including at least a subset of the plurality of characters and/or may generate at least one field-level bounding box indicating at least one field including at least a subset of the one or more words. The processor may overlay the at least one word-level bounding box and the at least one field-level bounding box on the image to form a masked image including a plurality of optically-recognized characters and one or more predicted fields for at least a subset of the plurality of optically-recognized characters.

A processor may receive an image and identify a plurality of characters in the image using a machine learning (ML) model. The processor may generate at least one word-level bounding box indicating one or more words including at least a subset of the plurality of characters and/or may generate at least one field-level bounding box indicating at least one field including at least a subset of the one or more words. The processor may overlay the at least one word-level bounding box and the at least one field-level bounding box on the image to form a masked image including a plurality of optically-recognized characters and one or more predicted fields for at least a subset of the plurality of optically-recognized characters.

Biometric data are obtained from biometric sensors on a stand-alone computing device, which may contain an ASIC, connected to or incorporated within it. The computing device and ASIC, in combination or individually, capture biometric samples, extract biometric features and match them to one or more locally stored, encrypted templates. The biometric matching may be enhanced by the use of an entered PIN. The biometric templates and other sensitive data at rest are encrypted using hardware elements of the computing device and ASIC, and/or a PIN hash. A stored obfuscated Password is de-obfuscated and may be released to the authentication mechanism in response to successfully decrypted templates and matching biometric samples. A different de-obfuscated password may be released to authenticate the user to a remote or local computer and to encrypt data in transit. This eliminates the need for the user to remember and enter complex passwords on the device.

A method for template matching can include iteratively selecting a template set of points to project over a centerline of a candidate symbol; conducting a template matching analysis; assigning a score to each template set; and selecting a template set with a highest assigned score. For example, the score can depend on proximity of the template points to a center and/or boundaries of a principal tracing path of the symbol. Additionally, one or more template sets having a top rank can be selected for a secondary analysis of proximity of the template points to a boundary of a printing of the symbol. The method can further include using the template with the highest score to interpret the candidate symbol.

Embodiments of a system and method for sorting and delivering articles in a processing facility based on image data are described. Image processing results such as rotation notation information may be included in or with an image to facilitate downstream processing such as when the routing information cannot be extracted from the image using an unattended system and the image is passed to an attended image processing system. The rotation notation information may be used to dynamically adjust the image before presenting the image via the attended image processing system.

An object management system includes an acquisition means for acquiring an image in which a surface of a registration target object, having a circle and a handwritten character drawn thereon, is captured, a generation means for detecting an ellipse corresponding to the circle from the image and generating a registration image in which the image is applied with projective transformation such that the ellipse becomes a circle, and a registration means for writing the registration image into a storage means as data for determining the sameness of the registration target object.

A text recognition method and apparatus that relate to the field of information processing technologies are provided. This effectively resolves a low recognition rate of curved text. The text recognition method includes: obtaining a to-be-detected image; determining a target text detection area in the to-be-detected image, where the target text detection area includes target text in the to-be-detected image, and the target text detection area is a polygonal area including m (m is a positive integer greater than 2) vertex pairs; correcting the polygonal area to m−1 rectangular areas to obtain a corrected target text detection area; and performing text recognition on the corrected target text detection area, and outputting the target text.

The image processing apparatus includes a reading unit configured to read a document and generate a scanned image, a dividing unit configured to analyze a distribution of constituent pixels of the scanned image and divide the scanned image based on a document component, an obtaining unit configured to obtain an inclination of a predetermined area among areas into which divided by the dividing unit, a classifying unit configured to classify the predetermined area into a predetermined area group based on the obtained inclination of the predetermined area, a setting unit configured to set a circumscribed rectangle encompassing the predetermined area included in the predetermined area group, a specifying unit configured to specify an area whose feature amount changes in the scanned image outward from the circumscribed rectangle as a document area, and a cropping unit configured to crop the specified document area as a document image.