SKCS-A Separable Kernel Family with Compact Support to improve visual segmentation of handwritten data

Abstract

Extraction of pertinent data from noisy gray level document images with various and complex backgrounds such as mail envelopes, bank checks, business forms, etc... remains a challenging problem in character recognition applications. It depends on the quality of the character segmentation process. 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) in scale space that achieved good performance in extracting data information with regard to the Gaussian kernel. In this paper, we focus in further improving the KCS efficiency by proposing a new separable version of kernel family namely (SKCS). This new kernel has also a compact support and preserves the most important properties of the Gaussian kernel in order to perform image segmentation efficiently and to make the recognizer task particularly easier. 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. Experiments, on real life data, for extracting handwritten data, from noisy gray level images, show promising performance of the SKCS kernel, especially in reducing drastically the processing time with regard to the KCS.