Training image-processing neural networks by synthetic photorealistic indicia-bearing images

A system, comprising:

• a memory;
• a processor, coupled to the memory, the processor configured to:
• generate an initial set of images, wherein each image of the initial set of images comprises a rendering of a text string;
• produce an augmented set of images by processing the initial set of images to introduce, into each image of the initial set of image, at least one simulated image defect;
• generate a training dataset comprising a plurality of pairs of images, wherein each pair of images comprises a first image selected from the initial set of images and a second image selected from the augmented set of images;
• and
• train, using the training dataset, a convolutional neural network for image processing.

A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to:

• generate an initial set of images, wherein each image of the initial set of images comprises a rendering of a text string;
• produce an augmented set of images by processing the initial set of images to introduce, into each image of the initial set of image, at least one simulated image defect;
• generate a training dataset comprising a plurality of pairs of images, wherein each pair of images comprises a first image selected from the initial set of images and a second image selected from the augmented set of images;
• and
• train, using the training dataset, a convolutional neural network for image processing.