Multiple field of view (FOV) systems are disclosed herein. An example system includes a bioptic barcode reader having a target imaging region. The bioptic barcode reader includes at least one imager having a first FOV and a second FOV and is configured to capture an image of a target object from each FOV. The example system includes one or more processors configured to receive the images and a trained object recognition model stored in memory communicatively coupled to the one or more processors. The memory includes instructions that, when executed, cause the one or more processors to analyze the images to identify at least a portion of a barcode and one or more features associated with the target object. The instructions further cause the one or more processors to determine a target object identification probability and to determine whether a predicted product identifies the target object.

The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

An unrecognized item code is identified during a transaction at a transaction terminal. When the unrecognized code is determined to be associated with a typographical error, alternative corrected item codes are supplied to the transaction terminal to replace the unrecognized code. When the unrecognized code is determined to be missing from a product catalogue, an ordered list of most likely corrected item codes for the unrecognized code is provided to the transaction terminal for selection of one of the corrected item codes by an operator.

The present invention teaches a solenoid-valve actuator that is battery-powered and communicates remotely and wirelessly (e.g., via LoRaWAN) to a gateway that communicates with the internet, thereby enabling a user to remotely control fluid flow through a solenoid valve. The end device interfaces to a range of latching solenoid operated valves, e.g., for the control of water flow in irrigation systems.

Disclosed are systems and methods for determining whether an unknown product matches a scanned barcode during checkout. The system includes a checkout lane having a flatbed scanning area with scanning devices and a point of sale (POS) terminal that scans a product identifier of an unknown product, identifies a product associated with the scanned product identifier, and transmits, to a computing system, product information. An overhead camera idnentifies, based on detecting an optical signal from the POS terminal, that a scanning event occurred, captures image data of the unknown product, and transmits, to the computing system, the image data. The computing system generates machine learning product identification models for identifying unknown products, identifies candidate product identifications for the unknown product based on applying the models to the image data, and determines, based on the candidate product identifications and the information about the product, whether the unknown product matches the product.

A store profile generation method includes moving a mobile base around a product facility which includes an arrangement of product display units for displaying products. Product labels are associated with the display units. The product labels display product-related data for the displayed products. With an image capture assembly mounted on the mobile base, images are acquired of the product display units at a sequence of locations of the mobile base. With a computer processor, the product-related data is extracted from the acquired images and a store profile is constructed, based on the extracted product-related data, a spatial characterization of the image capture assembly, and information on the locations of the mobile base when the images were acquired, which indicates locations of the product labels throughout the product facility.

Methods and apparatus to locate barcodes in color images are disclosed. An example method includes: obtaining a color digital image using an image sensor, the image including a plurality of pixels represented by respective ones of a plurality of luminance components and respective ones of a plurality of color components; determining, based on the luminance components, a plurality of barcode indicative characteristics for respective ones of a plurality of regions of the image; determining, based on the plurality of color components, a plurality of color content amounts for respective ones of the plurality of regions; identifying one or more regions of the plurality of regions that have their respective color content amount satisfy a first criteria, and their respective barcode indicative characteristic satisfy a second criteria; and processing image data corresponding to each of the one or more regions to attempt to identify one or more barcodes in the image.

For scanning optical patterns, such as two-dimensional QR codes, with a mobile device at increased distances, a first image is acquired. A region of interest likely containing the optical pattern in the first image is identified. The mobile device then zooms in on the region of interest and a second image is acquired. The optical pattern is then decoded using the second image.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.

Systems and methods for automatic identification and presentation of edges, shapes and unique objects in an image used for machine vision job setup are disclosed herein. An example method includes receiving, by one or more processors, an image file. The method further includes automatically determining, by the one or more processors, an item of interest within the image file. The method further includes analyzing, by the one or more processors, the item of interest to determine an appropriate tool for processing the item of interest. The method further includes displaying, by the one or more processors, on a display screen: (i) an image corresponding to the image file, (ii) an indication of the item of interest, and (iii) an indication of the appropriate tool.