Document Image Restoration Using Binary Morphological Filters.

This paper discusses a method for binary morphological filter design to restore document images degraded by subtractive or additive noise, given a constraint on the size of filters. With a filter size restriction (for example 3 by 3), each pixel in output image depends only on its (3 by 3) neighborhood of input image. Therefore, we can construct a look-up table between input and output. Each output image pixel is determined by this table. So the filter design becomes the search for the optimal look-up table. By considering the degradation condition of the input image, we provide a methodology for knowledge based look-up table design, to achieve computational tractability. The methodology can be applied iteratively so that the final output image is the input image after being transformed through successive 3 by 3 operations. An experimental protocol is developed for restoring degraded document images, and improving the corresponding recognition accuracy rates of an OCR algorithm. We present results for a set of real images which are manually ground-truthed. The performance of each filter is evaluated by the OCR accuracy.