Visual data extraction from bi-level document images using a generalized kernel family with compact support, in scale-space
Presents a generalization of a new kernel family with compact support in scale space which we recently published (Vision Interface, pp. 445-52, May 1999). We have shown that the proposed kernels are able to recover the information loss when using the Gaussian kernel, while they drastically reduce the processing time. Furthermore, the generalized kernel family preserves all the properties of the previous one and it offers other important properties, like the behavior of the first and second derivatives which are guaranteed to be the same as the Gaussian kernel one at any scale space. The latter property plays an important role in image processing, as we show in this paper. The construction and some properties shown in the previous version are recalled and some of the new properties of the new version are proven. An application of extracting handwritten data from noisy bi-level document images is presented to point out the practical impact of the improved version of the proposed kernels.