  Segmentation of a text-document into lines, words and characters, which is
considered to be the crucial pre-processing stage in Optical Character
Recognition (OCR) is traditionally carried out on uncompressed documents,
although most of the documents in real life are available in compressed form,
for the reasons such as transmission and storage efficiency. However, this
implies that the compressed image should be decompressed, which indents
additional computing resources. This limitation has motivated us to take up
research in document image analysis using compressed documents. In this paper,
we think in a new way to carry out segmentation at line, word and character
level in run-length compressed printed-text-documents. We extract the
horizontal projection profile curve from the compressed file and using the
local minima points perform line segmentation. However, tracing vertical
information which leads to tracking words-characters in a run-length compressed
file is not very straight forward. Therefore, we propose a novel technique for
carrying out simultaneous word and character segmentation by popping out column
runs from each row in an intelligent sequence. The proposed algorithms have
been validated with 1101 text-lines, 1409 words and 7582 characters from a
data-set of 35 noise and skew free compressed documents of Bengali, Kannada and
English Scripts.
