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In today's technological era, document images play an important and integralpart in our day to day life, and specifically with the surge of Covid-19,digitally scanned documents have become key source of communication, thusavoiding any sort of infection through physical contact. Storage andtransmission of scanned document images is a very memory intensive task, hencecompression techniques are being used to reduce the image size before archivaland transmission. To extract information or to operate on the compressedimages, we have two ways of doing it. The first way is to decompress the imageand operate on it and subsequently compress it again for the efficiency ofstorage and transmission. The other way is to use the characteristics of theunderlying compression algorithm to directly process the images in theircompressed form without involving decompression and re-compression. In thispaper, we propose a novel idea of developing an OCR for CCITT (TheInternational Telegraph and Telephone Consultative Committee) compressedmachine printed TIFF document images directly in the compressed domain. Aftersegmenting text regions into lines and words, HMM is applied for recognitionusing three coding modes of CCITT- horizontal, vertical and the pass mode.Experimental results show that OCR on pass modes give a promising results.

OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model