This article presents a system for acquisition, filtering, and segmentation of thermographic images in real time. Image acquisition is carried out using an infrared line scanner (IRLS), with which thermographic line scans are captured from hot strips while they are moving forward along a track. During the acquisition process, a relationship between each sample in the line scan and its position on the strip is established using a theoretical model of the IRLS, whose parameters have been adjusted using a calibration procedure. After the acquisition, line scans are filtered using a new signal operator designed to work in real time. Online with acquisition and filtering processes, segmentation is applied to the stream of line scans to group them into regions with similar temperature pattern. Two new segmentation algorithms based on well-known approaches, region merging and edge detection, have been designed to work in real time on a stream of line scans. The algorithms are evaluated using a novel segmentation assessment method based on the uncertainty of the ground truth, which can also be used for parameter tuning. Experimental results from a database of 200,000 images taken from manufactured steel strips over a period of three years demonstrate the efficiency and effectiveness of the proposed system.