1. A method of processing images with a trained Optical Character Recognition (OCR) system at a computer system, the method comprising: generating a training, set of data that associates a label corresponding to each symbol artifact of each of one or more symbol artifacts in the images with determined locations of the symbol artifacts with the images at the computer system; and presenting the training set of data to a neural network of the trained OCR system for training; wherein generating the training set of data further comprises producing a modified data file from a single image revision operation in which two or more locate-label data files are processed, such that each locate-label data file specifies an artifact location and a corresponding label for artifacts in the images, such that the single image revision operation resolves any discrepancy between the locate-label data files in which different labels are specified for a given artifact in the images by specifying one label across all the locate-label data files for the given artifact in the modified data file, and producing one or more corrected data files from a multiple image revision operation in which two or more modified data files are processed such that the multiple image revision operation resolves any discrepancy between the modified data files, such that the one or more corrected data files having changed labels for any outlier artifact-label pair comprise the training set of data.