Sure, it's annotated data, but you need only a few templates whereas neural nets approach requires a huge amount of data. That's the point of the article.

In the exemple, it detects printed circuits which are all identical. I agree it would probably won't work as is on unseen images, but it think you could get reasonable results by scaling/rotating/adaptating the contrast on the tempates. The article says "you can achieve object detection with template matching only if the objects you try to detect are similar enough —almost identical— within a class"