SKCS-new kernel family with compact support

Extraction of pertinent data from noisy document images with complex backgrounds remains a challenging problem in character recognition applications. It depends on the quality of the character segmentation. Over the last few decades, mathematical tools have been developed for this purpose. Several authors show that the Gaussian kernel is unique and offers many beneficial properties. In their recent work Remaki and Cheriet proposed a new kernel family with compact supports (KCS) that achieved good performance with accurate information extraction and reducing drastically time processing with regard to the Gaussian kernel. In this paper, we focus in further improving its efficiency by proposing a new separable version which itself has a compact support. Experiments, on real life data, from noisy gray level images, show fast and high performance with accurate results of such a kernel. A practical comparison is established between results obtained by using the KCS and the SKCS operators. Our comparison is based on the information loss and the gain in time processing.