Binarization of document images using hierarchical histogram equalization technique with linearly merged membership function

Abstract

Binarization itself is a process of finding a threshold value for converting a grey level image into a binary image. The threshold may vary depending on whether it is found globally or locally. It is found that either of the global and the local threshold itself can not provide a good binarization; rather a combination of the two is a better solution. In the current work, we have applied histogram equalization technique over the complete image and also over all the partitions of the image at different levels of hierarchy. A novel scheme is formulated for giving the membership value to each pixel at each level of hierarchy during histogram equalization. Then the image is binarized depending on the net membership value of each pixel. The technique outperforms when exhaustively tested on document images collected from different sources. © 2012 Springer-Verlag GmbH Berlin Heidelberg.