Digital Image Enhancement using Normalization Techniques and their application to Palm Leaf Manuscripts

Palm leaves were one of the earliest forms of writing media and their use as writing material in South and Southeast Asia has been recorded from as early as the fifth century B.C. until as recently as the late 19th century. Palm leaf manuscripts relating to art and architecture, mathematics, astronomy, astrology, and medicine dating back several hundreds of years are still available for reference today thanks to many ongoing efforts for preservation of ancient documents by libraries and universities around the world. Palm leaf manuscripts typically last a few centuries but with time the palm leaves degrade and the writing becomes illegible to be useful in any form. Image processing techniques can help enhance the images of these manuscripts so as to enable retrieval of the written text from these degraded documents. In this paper we propose a set of transform based methods for enhancing digital images of palm leaf manuscripts. The methods first approximate the background of a gray scale image using one of two models – piece-wise linear or nonlinear models. The background approximations are designed to overcome unevenness of document background. Then the background normalization algorithms are applied to the component channel images of a color palm leaf image. We also propose two local adaptive normalization algorithms for extracting enhanced gray scale images from color palm leaf images. The techniques are tested on a set of palm leaf images from various sources and the preliminary results show significant improvement in readability. The techniques can also be used to enhance images of ancient, historical, degraded papyrus and paper documents.