A method of operation for a processor-based device to identify a machine-readable symbol in an image, the processor-based device including at least one processor and at least one nontransitory processor-readable storage medium, the method comprising: receiving, in the at least one nontransitory processor-readable storage medium, a plurality of training images, each training image corresponding to one of a plurality of machine-readable symbology elements; generating, by the at least one processor, a distortion model for the training images; generating, by the at least one processor, a plurality of distorted image signals based at least in part on the distortion model and the training images, each of the plurality of distorted image signals corresponding to one of the machine-readable symbology elements; transforming, by the at least one processor, the plurality of distorted image signals from a signal space into a first transform space; extracting, by the at least one processor, classification features from the transformed distorted image signals in the first transform space; training, by the at least one processor, a first machine-readable symbology elements classifier using at least the extracted classification features; receiving a run-time image in the at least one nontransitory processor-readable storage medium; pre-processing, by the at least one processor, the received run-time image by identifying the portion of the received run-time image that corresponds to the machine-readable symbol and identifying within the machine-readable symbol sub-portions corresponding to individual symbology elements; transforming, by the at least one processor, image signals of the identified sub-portions from the signal space into the first transform space; extracting, by the at least one processor, classification features from the transformed image signals in the first transform space; and classifying the run-time image using the trained first machine-readable symbology elements classifier on each of the symbology elements using the extracted classification features. receiving, in the at least one nontransitory processor-readable storage medium, a plurality of training images, each training image corresponding to one of a plurality of machine-readable symbology elements; generating, by the at least one processor, a distortion model for the training images; generating, by the at least one processor, a plurality of distorted image signals based at least in part on the distortion model and the training images, each of the plurality of distorted image signals corresponding to one of the machine-readable symbology elements; transforming, by the at least one processor, the plurality of distorted image signals from a signal space into a first transform space; extracting, by the at least one processor, classification features from the transformed distorted image signals in the first transform space; training, by the at least one processor, a first machine-readable symbology elements classifier using at least the extracted classification features; receiving a run-time image in the at least one nontransitory processor-readable storage medium; pre-processing, by the at least one processor, the received run-time image by identifying the portion of the received run-time image that corresponds to the machine-readable symbol and identifying within the machine-readable symbol sub-portions corresponding to individual symbology elements; transforming, by the at least one processor, image signals of the identified sub-portions from the signal space into the first transform space; extracting, by the at least one processor, classification features from the transformed image signals in the first transform space; and classifying the run-time image using the trained first machine-readable symbology elements classifier on each of the symbology elements using the extracted classification features. <EOS>