Systems, methods and articles for reading highly blurred machine-readable symbols

摘要

Systems and methods for robust recognition of machine-readable symbols from highly blurred or distorted images. An image signal representation of a machine-readable symbol element is transformed into a different space using one or more transform operations, which moves an n-dimensional vector of measured light intensities into another n-dimensional space. The types of transform operations may include blur robust orthonormal bases, such as the Discrete Sine Transform, the Discrete Cosine Transform, the Chebyshev Transform, and the Lagrange Transform. A trained classifier (e.g., an artificial intelligence machine learning algorithm) may be used to classify the transformed signal in the transformed space. The types of trainable classifiers that may be used include random forest classifiers, Mahalanobis classifiers, support vector machines, and classification or regression trees.

主权项

1. 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 symbols; 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 symbols; 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 symbol classifier based at least in part on the extracted classification features; determining, by the at least one processor, a quality measure for the first machine-readable symbol classifier; transforming, by the at least one processor, the plurality of distorted image signals from the signal space into a second transform space; extracting, by the at least one processor, classification features from the distorted image signals in the second transform space; training, by the at least one processor, a second machine-readable symbol classifier based at least in part on the extracted classification features; determining, by the at least one processor, a quality measure for the second machine-readable symbol classifier; and selecting one of the first machine-readable symbol classifier or the second machine-readable symbol classifier based at least in part on the determined quality measure.