StreamShot achieves excellent recognition rates on small structures by extracting super-resolution contours directly from gray and color images. In contrast to pixel-based image data, quality and processing speed is independent of the orientation resulting in a highly flexible and robust detection. High-resolution contours can be used as ideal input for shape-based classifiers as well as for locating objects in any rotation.

When solving complex recognition tasks, it is difficult to achieve highest recognition and lowest error rates based on a single generic approach. Instead best-of-class solutions combine different and complementary algorithms in order to tackle a large variety of challenges. recosys has more than two decades of experience with tuning of recognition engines on large data sets. It has build a highly efficient and flexible environment for development including visual inspection and systematic logging.
Decision trees allow the deployment of incremental improvements with a maximum stability of the system’s behavior: Instead of delivering a newly trained generic classifier (with potentially unknown behavior), decision trees can be used for finely controlled specific error reduction.