Abstract: A method of activating an illumination assembly within a symbology reader is provided, the illumination assembly having a first illumination source and a second illumination source, the symbology reader having an imaging sensor configured to operate at a predetermined framerate where each frame includes an exposure period over which the imaging sensor is active to capture image data and a non-exposure period over which the imaging sensor is not active to capture image data, the method comprising: during a first frame, activating the first illumination source during at least a portion of the respective exposure period and activating the second illumination source over at least a portion of the respective non-exposure period; and during a second frame, activating the second illumination source during at least a portion of the respective exposure period and activating the first illumination source over at least a portion of the respective non-exposure period.

Abstract: A method of activating an illumination assembly within a symbology reader is provided, the illumination assembly having a first illumination source and a second illumination source, the symbology reader having an imaging sensor configured to operate at a predetermined framerate where each frame includes an exposure period over which the imaging sensor is active to capture image data and a non-exposure period over which the imaging sensor is not active to capture image data, the method comprising: during a first frame, activating the first illumination source during at least a portion of the respective exposure period and activating the second illumination source over at least a portion of the respective non-exposure period; and during a second frame, activating the second illumination source during at least a portion of the respective exposure period and activating the first illumination source over at least a portion of the respective non-exposure period.

Abstract: Techniques are provided for detecting an improper barcode using a neural network trained to identify an object from physical features appearing in images of object, and without resorting to using a barcode or other indicia to identify the object. The neural network is self-training, updating itself with selected images obtained at a Point-of-Sale. That is, the neural network is capable of training itself while performing improper barcode detection operations, such as spoofing detection.

Abstract: Techniques are provided for training a neural network, where the techniques include receiving image scan data of an object, such as a product or package presented at a scanning station, where the image scan data includes an image that contains at least one indicia corresponding to the object and physical features of the object. A neural networks examines the physical features and determines weighting indicating a correlation strength between the physical feature and an identification data of the object. Thereby training a neural network to identify objects from their scanned physical features in place or in accompaniment to scanned indicia data.

Abstract: Techniques are provided for detecting an improper barcode using a neural network trained to identify an object from physical features appearing in images of object, and without resorting to using a barcode or other indicia to identify the object. The neural network is self-training, updating itself with selected images obtained at a Point-of-Sale. That is, the neural network is capable of training itself while performing improper barcode detection operations, such as spoofing detection.