An object recognition method related to the field of artificial intelligence comprises: collecting an object to be subjected to recognition (S101); according to a target text detection model corresponding to the object to be subjected to recognition, carrying out screening and recognition on full text information corresponding to the object to be subjected to recognition, so as to obtain point-of-interest text information therefrom (S102); and carrying out recognition on the point-of-interest text information according to a preset text recognition model (S103). A target text detection model obtains point-of-interest text information by means of carrying out screening and recognition on full text information, such that the recognition of full text information in the prior art is avoided, thus saving recognition time, and improving the recognition efficiency.

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.

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.

Disclosed are methods and systems for using artificial intelligence (AI) for image recognition by using predefined coordinates to extract a portion of a received image, the extracted portion comprising a word to be identified having at least a first letter and a second letter; executing an image recognition protocol to identify the first letter; when the server is unable to identify the second letter, the server executes an AI model having a nodal data structure to identify the second letter based upon the identified first letter, the nodal data structure comprising a set of nodes where each node represents a letter, each node connected to at least one other node, wherein connection of a first node to a second node corresponds to a probability that a letter corresponding to the second node is used in a word subsequent to a letter corresponding to the first node.

Methods, apparatus, systems and articles of manufacture are disclosed for receipt decoding. An example apparatus includes processor circuitry to execute instructions to extract text from the receipt image, the text including bounding boxes; associate ones of the bounding boxes to link horizontally related fields of a the receipt image by selecting a first bounding box; identifying first horizontally aligned bounding boxes, the first horizontally aligned bounding boxes to include at least one bounding box of the bounding boxes that is horizontally aligned relative to the first bounding box; adding the first horizontally aligned bounding boxes to a word sync list; and connecting ones of the first horizontally aligned bounding boxes and the first bounding box based on at least one of an amount of the first horizontally aligned bounding boxes in the word sync list and a relationship among the first horizontally aligned bounding boxes and the first bounding box.

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 image forming apparatus includes: a document reading device that reads a plurality of original documents on a document support surface in a batch; an individual image cutouter that cuts individual images of the original documents out of image data obtained by batch reading; a character recognizer that recognizes, for each individual image, characters in the individual image; a document determiner that determines, for each individual image, whether the recognized characters contain a type name; an acquirer that acquires, from the characters determined to contain the type name, a plurality of informative character strings associated one-to-one with a plurality of item names; a data generator that generates, for each individual image, a piece of document data in which the type name is associated with the plurality of acquired informative character strings; and a document data storage that stores the pieces of document data generated one for each individual image.

A control device that controls a control target apparatus including a moving part and an imaging device that changes its position relative to a target as the moving part moves and that acquires a captured image of the target, includes a drive unit that drives the moving part, based on a drive command signal to move the moving part to a target position, a relative position estimation unit that calculates an estimated value of a relative position between the target and the imaging device, based on the drive command signal, a template image correction unit that corrects a preregistered template image, based on a time-series signal of the estimated value of the relative position within an imaging time of the imaging device, and a target position correction unit that corrects the target position using the corrected template image.

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.