A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

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.

An inspection apparatus according to exemplary embodiments of the present disclosure includes a reception unit configured to receive print data, a registration unit configured to register an image included in the print data as a reference image, an extraction unit configured to extract information relating to a barcode inspection from the received print data, and an inspection unit configured to inspect, when a scanned image generated by scanning a print product is received, a barcode in the scanned image, based on the extracted information relating to the barcode inspection.

Systems and methods are provided for: receiving an image containing a code that has one or more visual qualities that fail to satisfy respective thresholds; applying a trained machine learning model to find a rough location of the code by generating a bounding box and cropping out the portion of the image; applying another trained machine learning model to the portion of the image to estimate key point locations of the code depicted in the portion of the image, aligning the portion of the image that depicts the code based on the estimated key point locations; and decoding, by the other trained machine learning model, the aligned portion of the image that depicts the code.

Systems and methods for establishing optimal reading conditions for a machine-readable symbol reader. A machine-readable symbol reader may selectively control reading conditions including lighting conditions (e.g., illumination pattern), focus, decoder library parameters (e.g., exposure time, gain), etc. Deep learning and optimization algorithms (e.g., greedy search algorithms) are used to autonomously learn an optimal set of reading parameters to be used for the reader in a particular application. A deep learning network (e.g., a convolutional neural network) may be used to locate machine-readable symbols in images captured by the reader, and greedy search algorithms may be used to determine a reading distance parameter and one or more illumination parameters during an autonomous learning phase of the reader. The machine-readable symbol reader may be configured with the autonomously learned reading parameters, which enables the machine-readable symbol reader to accurately and quickly decode machine-readable symbols (e.g., direct part marking (DPM) symbols).

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.

To suppress an increase in processing time due to a load of inference processing while improving reading accuracy by the inference processing of machine learning. An optical information reading device includes a processor including: an inference processing part that inputs a code image to a neural network and executes inference processing of generating an ideal image corresponding to the code image; and a decoding processing part that executes first decoding processing of decoding the code image and second decoding processing of decoding the ideal image generated by the inference processing part. The processor executes the inference processing and the first decoding processing in parallel, and executes the second decoding processing after completion of the inference processing.

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.

Embodiments provide methods and systems for verifying authenticity of products. In an embodiment, an image of at least a part of a product label of a product is scanned and processed. An image profile is created from the scanned image and compared with a set of reference image profiles. Each reference image profile is associated with a reference image, a reference control transform value and a reference validation transform value. If there is a matching between the image profile and one of the reference image profiles, the reference image corresponding to the matching reference image profile is retrieved. A control transform and a validation transform of the scanned image is determined. The control transform value and the validation transform value are compared with the reference control transform vale and the reference validation transform value of the reference image for verifying authenticity of the products, respectively.