OCR for laser marking quality assessment

Abstract

Since 2020 in the USA1 and 2021 in Europe, all medical devices have to be marked with a Unique Device Identification (UDI) code to ensure their traceability. UDI codes are laser marked but the engraving process is error-prone due laser-related or external conditions. Defects may be assessed visually but this process is costly and gives rise to human errors. Using machine vision to perform this task for large batches of UDI codes may be challenging due to alterations in readability caused by marking defects or image quality. Therefore, we have tested several learned methods to achieve two goals: correctly recognize characters and identifying marking defects on UDI codes. As the codes were engraved on cylindrical metallic surfaces with a metallic paint effect, we had to address the problem of specular and stray reflections through the development of a tailor-made lighting engine. Our image grabbing and processing pipeline comprises of an imaging device designed to prevent reflections onto engraved codes; an Optical Character Recognition (OCR) algorithm (multilayer perceptron, support vector machine, classical image segmentation), and a probabilistic model to detect faulty characters that need to be further qualified by a human operator. Our results show that multilayer perceptron (MLP) and support vector machine (SVM) recognition performances are very close together and above classical image segmentation.