  In today's technological era, document images play an important and integral
part in our day to day life, and specifically with the surge of Covid-19,
digitally scanned documents have become key source of communication, thus
avoiding any sort of infection through physical contact. Storage and
transmission of scanned document images is a very memory intensive task, hence
compression techniques are being used to reduce the image size before archival
and transmission. To extract information or to operate on the compressed
images, we have two ways of doing it. The first way is to decompress the image
and operate on it and subsequently compress it again for the efficiency of
storage and transmission. The other way is to use the characteristics of the
underlying compression algorithm to directly process the images in their
compressed form without involving decompression and re-compression. In this
paper, we propose a novel idea of developing an OCR for CCITT (The
International Telegraph and Telephone Consultative Committee) compressed
machine printed TIFF document images directly in the compressed domain. After
segmenting text regions into lines and words, HMM is applied for recognition
using three coding modes of CCITT- horizontal, vertical and the pass mode.
Experimental results show that OCR on pass modes give a promising results.
