  JPEG is one of the popular image compression algorithms that provide
efficient storage and transmission capabilities in consumer electronics, and
hence it is the most preferred image format over the internet world. In the
present digital and Big-data era, a huge volume of JPEG compressed document
images are being archived and communicated through consumer electronics on
daily basis. Though it is advantageous to have data in the compressed form on
one side, however, on the other side processing with off-the-shelf methods
becomes computationally expensive because it requires decompression and
recompression operations. Therefore, it would be novel and efficient, if the
compressed data are processed directly in their respective compressed domains
of consumer electronics. In the present research paper, we propose to
demonstrate this idea taking the case study of printed text line segmentation.
Since, JPEG achieves compression by dividing the image into non overlapping 8x8
blocks in the pixel domain and using Discrete Cosine Transform (DCT); it is
very likely that the partitioned 8x8 DCT blocks overlap the contents of two
adjacent text-lines without leaving any clue for the line separator, thus
making text-line segmentation a challenging problem. Two approaches of
segmentation have been proposed here using the DC projection profile and AC
coefficients of each 8x8 DCT block. The first approach is based on the strategy
of partial decompression of selected DCT blocks, and the second approach is
with intelligent analysis of F10 and F11 AC coefficients and without using any
type of decompression. The proposed methods have been tested with variable font
sizes, font style and spacing between lines, and a good performance is
reported.
