A set of playing cards comprises N playing cards, N>1, each playing card having a detectable card identity string. This card identity string is a series of M characters. The set of playing cards is provided with a set identity string, this set identity string being composed of N groups of R characters, R<=M. Each card identity string provides to the set identity string one group of R characters taken from the series of M characters of this card identity string. The sequence of said N groups of R characters in the set identity string is identical to the sequence of the N cards in the set of playing cards.

A method of logistics management based on QR code, the method includes: acquiring a video stream of a logistics box taken by a camera of a logistics box passing through an identification area, the logistics box having a corresponding QR code affixed to its surface; performing target detection on the video stream based on a predetermined neural network model to obtain a QR code image and a logistics box image, respectively; identifying the QR code image to generate QR code information; detecting and tracking the logistics box images in sequence to generate logistics box information; matching the QR code information and logistics box information, generating matching information, and updating logistics inventory information based on the matching information.

Techniques for automatically extracting data from documents using multiple deep learning models are provided. According to one set of embodiments, a computer system can receive a document in an electronic format and can segment, using an image segmentation deep learning model, the document into a plurality of segments, where each segment corresponds to a visually discrete portion of the document and is classified as being one of a plurality of types. The computer system can then, for each segment in the plurality of segments, retrieve text in the segment using optical character recognition (OCR) and extract data in the segment from the retrieved text using a named entity recognition (NER) deep learning model, where the retrieving and the extracting are performed in a manner that takes into account the segment's type.

Real-time video is captured of a pickup area for orders at a store. The images are analyzed and tracked for unique orders being placed in the pickup area and orders being removed from the pickup area. A customer-operated device is operated by a customer to identify the store where the customer placed an order in a remote location from the pickup area. Images of the orders that are present within the pickup area and order identifying information for the orders are provided to the customer via the customer-operated device.

Provided is a setting management system that makes setting operation of a device efficient. A setting terminal includes a storage unit that stores a scenario of a setting process related to a device to be set, an input unit that is USB connected to the device to be set and inputs information to the device to be set, a progress determination unit that determines a progress of a scenario of a setting process based on timing of an input by the input unit and the scenario stored in the storage unit, and a setting operation unit that inputs, to the device to be set, setting information via the input unit according to the progress of the scenario determined by the progress determination unit.

A method of controlling an image processing apparatus includes: obtaining a candidate image group including a plurality of images; determining a specific condition for preferentially selecting an image from the candidate image group: analyzing the images in the candidate image group; analyzing captions attached to the images in the candidate image group; and selecting a specific image from the candidate image group based on results of the determining the specific condition, the analyzing the images, and the analyzing the captions.

A medicine image recognition method applied to an electronic device is provided. The method includes obtaining target images by inputting medicine images into a position detection network. Character feature matrices are generated according to the target images and a character recognition network. Image feature matrices are generated by inputting the target images into a category recognition network. Reference matrices are generated according to the image feature matrices and corresponding character feature matrices. Once a matrix to be tested is generated by processing an image to be tested, and a recognition result of the image to be tested is generated according to a similarity between the matrix to be tested and each of the reference matrices.

Document classification techniques are disclosed that convert text content extracted from documents into graphical images and apply image classification techniques to the images. A graphical image of the text (such as a bar-code) may be generated and applied to improve the performance of document classification, bypassing NLP and utilizing more efficient localized OCR than in conventional approaches.

A system and method for field extraction including determining a key position of a key in an electronic file, isolating candidate key values based on a distance from the key position, selecting a key value from the candidate key values based on an output of a trained neural network, and extracting the key and the key value from the electronic file, regardless of a key-value structure.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.