A commodity sales data processing apparatus includes an image processor and a controller. The image processor captures an image including a symbol. The symbol includes discount information relating to a discount on a price of a commodity. The controller acquires commodity information that uniquely specifies the commodity. The controller extracts a region from the image captured by the camera. The region includes the symbol. The controller transmits, to a server, an image of the region extracted by the controller. The controller acquires, from the server, the discount information based on the image transmitted to the server. The controller registers the commodity information acquired by the controller and a discounted price of the commodity based on the discount information acquired by the controller in association with each other.

A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

An object tracking system that includes a sensor, a weight sensor, and a tracking system. The sensor configured to capture a frame of at least a portion of a rack within a global plane for a space. The tracking system is configured to detect a weight decrease on the weight sensor. The tracking system is further configured to receive the frame of the rack, to identify a marker on an item within a predefined zone in the frame, and to identify the item associated with the identified marker. The tracking system is further configured to determine a pixel location for a person, to determine the person is within the predefined zone associated with the, and to add the identified item to a digital cart associated with the person.

A community mapping platform may receive an image that depicts a community layout of a community and may process, using a computer vision model, the image to identify a unit, of the community, that is depicted in the image (e.g., based on identifying a text string and/or a polygon in the image). The community mapping platform may determine sets of community geographical coordinates for a set of reference locations of the community and may map the sets of community geographical coordinates to corresponding reference pixel locations of the image. The community mapping platform may determine, using a geographical information system, unit geographical coordinates of the unit based on the reference pixel locations and may perform an action associated with the unit geographical coordinates.

An object tracking system that includes a sensor and a tracking system. The sensor configured to capture a frame of at least a portion of a rack within a global plane for a space. The tracking system is configured to detect an item was removed from the rack. The tracking system is further configured to receive the frame of the rack, to identify a marker on an item within a predefined zone in the frame, and to identify the item associated with the identified marker. The tracking system is further configured to determine a pixel location for a person, to determine the person is within the predefined zone associated with the, and to add the identified item to a digital cart associated with the person.

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.

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.

A method, an apparatus, a device and a computer-readable storage medium for recognizing aerial handwriting are provided. The method may include detecting a palm region of a user in a two-dimensional grayscale image; detecting a fingertip in the two-dimensional gray-scale image based on the palm area; determining a spatial trajectory of the fingertip based on a set of two-dimensional gray-scale images, the set of two-dimensional gray-scale images including the two-dimensional gray-scale image; and recognizing handwritten content of the user based on the spatial trajectory. A two-dimensional gray-scale image is used to recognize and track the spatial trajectory of the fingertip, which may speed up aerial handwriting recognition, and has low processing performance requirements for the device, while also ensuring high accuracy.

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.