Optical music recognition aims to convert the vast repositories of sheet music in the world into an on-line digital format. In the near future it will be possible to assimilate music into digital libraries and users will be able to perform searches based on a sung melody in addition to typical text-based searching. An important requirement for such a system is the ability to reproduce the original score as accurately as possible. Due to the huge amount of sheet music available, the efficient storage of musical images is an important topic of study. This paper investigates whether the "knowledge" extracted from the optical music recognition (OMR) process can be exploited to gain higher compression than the JBIG international standard for bi-level image compression. We present a hybrid approach where the primitive shapes of music extracted by the optical music recognition process-note heads, note stems, staff lines and so forth-are fed into a graphical symbol based compression scheme originally designed for images containing mainly printed text. Using this hybrid approach the average compression rate for a single page is improved by 3.5% over JBIG. When multiple pages with similar typography are processed in sequence, the file size is decreased by 4-8%. The relevant background to both optical music recognition and textual image compression is presented. Experiments performed on 66 test images are described, outlining the combinations of parameters that were examined to give the best results.

An essential component of many applications in natural language processing is a language modeler able to correct errors in the text being processed. For optical character recognition (OCR), poor scanning quality or extraneous pixels in the image may cause one or more characters to be mis-recognized, while for spelling correction, two characters may be transposed, or a character may be inadvertently inserted or missed out, This paper describes a method for correcting English text using a PPM model. A method that segments words in English text is introduced and is shown to be a significant improvement over previously used methods. A similar technique is also applied as a post-processing stage after pages have been recognized by a state-of-the-art commercial OCR system. We show that the accuracy of the OCR system can be increased from 96.3% to 96.9%, a decrease of about 14 errors per page.

Most bi-level images stored on computers today comprise scanned text, and are stored using generic bi-level image technology based either on classical run-length coding, such as the CCITT Group 4 method, or on modern schemes such as JBIG that predict pixels from their local image context. However, image compression methods that are tailored specifically for images known to contain printed text can provide noticeably superior performance because they effectively enlarge the context to the character level, at least for those predictions for which such a context is relevant. To deal effectively with general documents that contain text and pictures, it is necessary to detect layout and structural information from the image, and employ different compression techniques for different parts of the image. The authors extend previous work in document image compression in two ways. First, we include automatic discrimination between text and non-text zones in an image. Second, the system is tested on a large real-world image corpus.

Textual image compression is a method of both lossy and lossless image compression that is particularly effective for images containing repeated sub-images, notably pages of text. This paper addresses the problem of pattern comparison by using an information or compression based approach. Following Mohiuddin et al. ( 1984), the authors use the amount of uncertainty or entropy between marks as the criterion for the matching process. The entropy model they use is the context-based compression model proposed by Langdon and Rissanen (1981) and further developed by Moffat (1991). There are two principal issues to investigate when studying template matching methods: their susceptibility to different kinds of noise, and how they respond to errors in the initial registration. Because of the computation-intensive nature of the comparison operation, many schemes have been devised to pre-filter or screen the marks in advance to determine those that will surely fail the match. They present a novel method of screening which uses a quad-tree decomposition and finds local centroids at each tree level.>

A two-stage method for compressing bilevel images is described that is particularly effective for images containing repeated subimages, notably text. In the first stage, connected groups of pixels, corresponding approximately to individual characters, are extracted from the image. These are matched against an adaptively constructed library of patterns seen so far, and the resulting sequence of symbol identification numbers is coded and transmitted. From this information, along with the library itself and the offset from one mark to the next, an approximate image can be reconstructed. The result is a lossy method of compression that outperforms other schemes. The second stage employs the reconstructed image as an aid for encoding the original image using a statistical context-based compression technique. This yields a total bandwidth for exact transmission appreciably undercutting that required by other lossless binary image compression methods. Taken together, the lossy, and lossless methods provide an effective two-stage progressive transmission capability for textual images which has application for legal, medical, and historical purposes, and to archiving in general.>