Readout of optically readable codes

A method of training a machine learning model, comprising:

• providing training data for a plurality of objects, the training data for each object of the plurality of objects comprising:
• i) at least one reference image of an optical code which is incorporated into a surface of the object of the plurality of objects;
• and
• ii) a transformed reference image of the optical code;
• wherein decoding of the optical code in the transformed reference image produces fewer decoding errors than the decoding of the optical code in the reference image;
• providing a machine learning model, wherein the machine learning model is configured to generate a second image based on at least a first image and based on model parameters;
• training the machine learning model, the training comprising for each object of the plurality of objects:
• inputting the at least one reference image into the machine learning model;
• receiving a predicted transformed reference image from the machine learning model;
• calculating a deviation between the transformed reference image and the predicted transformed reference image;
• modifying the model parameters to reduce the deviation;
• storing and/or outputting the trained machine learning model;
• and/or transmitting the trained machine learning model to a separate computer system;
• and
• and/or using the trained machine learning model to generate a transformed image of at least one new image of a different object.