ABSTRACT
With the surge of COVID-19 pandemic, the world is moving towards digitization and automation more than it was presumed. The Internet is becoming one of the popular mediums for communication, and multimedia (image, audio, and video) combined with data compression techniques play a pivotal role in handling a huge volume of data that is being generated on a daily basis. Developing novel algorithms for automatic analysis of compressed data without decompression is the need of the present hour. JPEG is a popular compression algorithm supported in the digital electronics world that achieves compression by dividing the whole image into non-overlapping blocks of 8 × 8 pixels, and subsequently transforming each block using Discrete Cosine Transform (DCT). This research paper proposes to carry out Fast and Smooth Segmentation (FastSS) directly in JPEG compressed printed text document images at text-line and word-level using DC and AC signals. From each 8 × 8 block, DC and AC signals are analyzed for accomplishing Fast and Smooth segmentation, and subsequently, two Faster segmentation (MFastSS) algorithms are also devised using low resolution-images generated by mapping the DC signal (DC Reduced Image) and encoded DCT (ECM Image) coefficients separately. Proposed models are tested on various JPEG compressed printed text document images created with varied space and fonts. The experimental results have demonstrated that the direct analysis of compressed streams is computationally efficient, and has achieved speed gain more than 90% when compared to uncompressed domains.
ABSTRACT
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. © 2022 IEEE.