  With the rapid increase in the volume of Big data of this digital era, fax
documents, invoices, receipts, etc are traditionally subjected to compression
for the efficiency of data storage and transfer. However, in order to process
these documents, they need to undergo the stage of decompression which indents
additional computing resources. This limitation induces the motivation to
research on the possibility of directly processing of compressed images. In
this research paper, we summarize the research work carried out to perform
different operations straight from run-length compressed documents without
going through the stage of decompression. The different operations demonstrated
are feature extraction; text-line, word and character segmentation; document
block segmentation; and font size detection, all carried out in the compressed
version of the document. Feature extraction methods demonstrate how to extract
the conventionally defined features such as projection profile, run-histogram
and entropy, directly from the compressed document data. Document segmentation
involves the extraction of compressed segments of text-lines, words and
characters using the vertical and horizontal projection profile features.
Further an attempt is made to segment randomly a block of interest from the
compressed document and subsequently facilitate absolute and relative
characterization of the segmented block which finds real time applications in
automatic processing of Bank Cheques, Challans, etc, in compressed domain.
Finally an application to detect font size at text line level is also
investigated. All the proposed algorithms are validated experimentally with
sufficient data set of compressed documents.
