Direct Processing of Document Images in Compressed Domain

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.