Learning method includes performing a first process in which a coarse class classifier configured with a first neural network is made to classify a plurality of images given as a set of images each attached with a label indicating a detailed class into a plurality of coarse classes including a plurality of detailed classes and is then made to learn a first feature that is a feature common in each of the coarse classes, and performing a second process in which a detailed class classifier, configured with a second neural network that is the same in terms of layers other than the final layer as but different in terms of the final layer from the first neural network made to perform the learning in the first process, is made to classify the set of images into detailed classes and learn a second feature of each detailed class.

A system for verifying a machine-readable label comprises a scan table processing device comprising a first input for receiving a list of items with machine-readable labels; a second input for receiving a list of stores that have an inventory of the items in the list of items and that have at least one sensing device for capturing images of the items; and an output that includes a plurality of electronic records. The system further comprises a data repository that stores the captured images of the items and that updates the electronic records to include an association to the captured images; a graphical user interface (GUI) processing apparatus that modifies the captured images in preparation for training an artificial intelligence apparatus to identify the items in the images; and a machine language (ML) model processor that determines whether the images training the artificial intelligence apparatus are correctly identified with machine-readable labels associated with the items.

A barcode reader configured to capture interleaved frame types optimized for vision capture and barcode capture are disclosed herein. An example barcode reader is configured to operate in a pre-determined repetitive pattern of capturing a first frame and capturing a second frame over a reading cycle having a fixed duration after a triggering event, wherein the first frame is captured over a first exposure period having a first duration, and the second frame is captured over a second exposure period having a second duration, and wherein the first frame is associated with a first brightness parameter, and the second frame is associated with a second brightness parameter.

A decoding system for an optical identifier includes a feature detection module configured to locate an optical identifier on an item in a captured image. The optical identifier contains encoded data, the encoded data includes information about the item, the optical identifier includes a plurality of cells, and each of the plurality of cells has one of a first state and a second state. An enhancement module configured to generate an enhanced optical identifier by selectively mapping each of the plurality of cells to one of the first state and the second state based on states of neighboring ones of the plurality of cells. A decoder module is configured to decode the encoded data contained in the enhanced optical identifier to output the information about the item.

A method of locating code image zones in an output image of a code bearing object (14), wherein first candidates for code image zones are determined in a first segmentation process using a process of classical image processing without machine learning and second candidates for code image zones are determined in a second segmentation process using machine learning, with the first and second candidates being fused to locate the code image zones.

Methods and apparatus to locate and decode an arranged plurality of barcodes in an image are disclosed. An example method includes obtaining image data representing an image of an environment appearing within a FOV of an imaging device that includes the image sensor, wherein an arranged plurality of barcodes appear in the image. A first subset of the plurality of barcodes is decoded from the image data. One or more parameters representing a predicted arrangement of the plurality of barcodes in the image is determined based upon location information associated with each of the decoded first subset of the plurality of barcodes. Possible locations for respective ones of a second subset of the plurality of barcodes are determined based upon the one or more parameters, and the second subset of the plurality of barcodes are attempted to be decoded from the image data in vicinities of the respective possible locations.

A method for traceability of raw materials or objects exposed to operational conditions in industry, including coding phasea and decoding phase. The coding phase includes steps of uploading a design matrix file to a Cdot API, the Cdot matrix is a digital decomposition part of the coding phase, coding parameter inputs of the design matrix; generating a Cdot matrix by embedding a codeword using a Cdot matrix calculation algorithm. The decoding phase includes providing the Cdot matrix to a reader device; creating a Cdot matrix image from a raw image of a material or object or product having a Cdot matrix on a surface captured by a camera; decoding coded values in a code area of the Cdot matrix image to extract an assertive code; interpreting the assertive code to determine a unique object or material identification definition; providing the the object or material identification definition to a display.

An optoelectronic code reader (10) having at least one light receiving element (24) for generating image data from reception light and an evaluation unit (26) with a classifier (30) being implemented in the evaluation unit (26) for assigning code information to code regions (20) of the image date, wherein the classifier (30) is configured for machine learning and is trained by means of supervised learning based on codes read by a classic decoder (28) which does not make use of methods of machine learning.

Methods, apparatuses and computer program products for providing artificial-intelligence-based indicia data editing are provided. For example, an example computer-implemented method may include determining, based at least in part on a data processing model associated with a scan setting module, a first decoded data string corresponding to a first indicia; determining, based at least in part on user input data, a first input data string corresponding to the first indicia; generating a predictive indicia data editing model based at least in part on providing the first decoded data string and the first input data string to an artificial intelligence algorithm; and updating the scan setting module based at least in part on the predictive indicia data editing model.

A barcode reader configured to capture interleaved frame types optimized for vision capture and barcode capture are disclosed herein. An example barcode reader is configured to operate in a pre-determined repetitive pattern of capturing a first frame and capturing a second frame over a reading cycle having a fixed duration after a triggering event, wherein the first frame is captured over a first exposure period having a first duration, and the second frame is captured over a second exposure period having a second duration, and wherein the first frame is associated with a first brightness parameter, and the second frame is associated with a second brightness parameter.