Process of automatic generation of images for training a machine learning system associated with a printing infrastructure

A digital printer comprising:

• a printing station having one or more printheads configured to eject ink on sheet material;
• an optical sensor located downstream the one or more printheads with respect to a direction of movement of the sheet material, the optical sensor being configured to generate one or more signals representative of an image printed on the sheet material;
• a control unit connected to the optical sensor, wherein the control unit comprises a trained machine learning system installed thereon, wherein the trained machine learning system is configured to:
• receiving the one or more signals generated by the optical sensor of the digital printer;
• determining one or more image samples of the printed image as a function of the one or more signals received by the optical sensor;
• determining an alarm condition depending on the one or more image samples by performing a comparison of the one or more image samples with one or more rendered images;
• in response to determining the alarm condition, identifying a defective nozzle of printheads of the digital printer based on the alarm condition;
• and
• sending an instruction to deactivate the defective nozzle and/or to emit an alarm signal.

A computer implemented method for automatic generation of training images for a machine learning system for image recognition, the machine learning system being usable by a digital printer, wherein the method comprises:

• obtaining, from at least one database of images, a series of initial digital images;
• applying to each of the initial digital images a first printing error representative of a given malfunction of the digital printer when printing a digital image on physical material;
• automatically tagging each of the initial digital images with the first printing error to obtain a series of tagged images;
• and
• graphically manipulating each of the tagged initial digital images to obtain, from each of the tagged images, one or more rendered images.