As the amount of digital information increases rapidly, the ability to store, transmit, or process digital data efficiently becomes a significant issue. Any further improvement in handling digital information, beyond technological limits in storage capacity and communication bandwidth, should come from manipulating compressed data. In the first part of this project, execution of common operations in document image understanding and OCR on CCITT-G4 compressed data is examined. These operations include: blob coloring, thinning, and morphological techniques. Direct manipulation of JBIG-coded documents with and without progressive coding is another objective of this project. For JBIG data, skew estimation and connected component algorithms are investigated. In addition, image processing algorithms for compressed binary document images and engineering drawings are implemented. The second part of this project deals with the feasibility of developing a new source code that allows quick string matching (exact and approximate) for OCR-generated text data. Furthermore, a new lossless binary image compression scheme that allows fast skew estimation for image rotation is developed. Algorithms developed under this project should contribute to efficient utilization of storage and bandwidth in information, computation and telecommunication technologies.
