  From the literature, it is demonstrated that performing text-line
segmentation directly in the run-length compressed handwritten document images
significantly reduces the computational time and memory space. In this paper,
we investigate the issues of word and character segmentation directly on the
run-length compressed document images. Primarily, the spreads of the characters
are intelligently extracted from the foreground runs of the compressed data and
subsequently connected components are established. The spacing between the
connected components would be larger between the adjacent words when compared
to that of intra-words. With this knowledge, a threshold is empirically chosen
for inter-word separation. Every connected component within a word is further
analysed for character segmentation. Here, min-cut graph concept is used for
separating the touching characters. Over-segmentation and under-segmentation
issues are addressed by insertion and deletion operations respectively. The
approach has been developed particularly for compressed handwritten English
document images. However, the model has been tested on non-English document
images.
