Difference of Boxes Filters Revisited: Shadow Suppression and Efficient Character Segmentation

Abstract

A robust segmentation is the most important part of an automatic character recognition system (e.g. document processing, license plate recognition etc.). In our contribution we present an efficient segmentation framework using a preprocessing step for shadow suppression combined with a local thresholding technique. The method is based on a combination of difference of boxes filters and a new ternary segmentation, which are both simple low-level image operations. We also draw parallels to a recently published work on a ganglion cell model and show that our approach is theoretically more substantiated as well as more robust and more efficient in practice. Systematic evaluation of noisy input data as well as results on a large dataset of license plate images show the robustness and efficiency of our proposed method. Our results can be applied easily to any optical character recognition system resulting in an impressive gain of robustness against nonlinear illumination.