In this paper we describe a scheme to enhance the usability of a Tablet PC's handwriting recognition system by
including medical symbols that are not a part of the Tablet PC's symbol library. The goal of this work is to make
handwriting recognition more useful for medical professionals accustomed to using medical symbols in medical records.
To demonstrate that this new symbol recognition module is robust and expandable, we report results on both a medical
symbol set and an expanded symbol test set which includes selected mathematical symbols.
Acquiring foreign language from degraded hardcopy documents is of interest to military and border control applications. Bi-tonal image scans are desirable because file size is small. However, the nature of hardcopy degradations and the scanner or image enhancement software capabilities used directly affect the quality of the captured image and the extent of language acquisition. We applied a collection of manual treatments to hardcopy Arabic documents to develop a corpus of bi-tonal images. We then used this corpus in an exploratory study to derive conclusions about how bi-tonal images could be enhanced. This paper discusses the manually degraded Arabic document corpus, the image enhancement study, and the significant optical character recognition (OCR) improvements obtained with simple scanner driver adjustments.
In this paper, we propose a new type of modified trimmed mean (MTM) filter for image smoothing. The MTM filter was first proposed by Lee and Kassam. The filter is designed to remedy the problem of edge blurring resulted by a mean filtering. The idea is to perform the averaging operation on some selected samples inside a window. A data sample is selected if its value falls into the range of (m - q, m + q) where m is a value calculated from the data samples and q is a preselected threshold value. Lee et al used the median filter to estimate the m value. Although the MTM filter works well for some images, it cannot preserve the details. This is because the median filter is not a detail preserving filter. In this paper, we propose to replace the median filter by a detail preserving filter, namely multistage median (MSM), for the m value estimation. We call this filter the multistage median based MTM (MSMTM) filter. It is shown that the new MSMTM filter is highly efficient and detail- preserving. By some modification, the MSMTM can also be used to filter the multiplicative noise. Finally, simulations are carried out to evaluate the performance of the filter.
In this paper we have proposed a new type of filter which has the most desirable properties of an image smoothing filter. These properties are (1) Robust smoothing efficiency. (2) Edge preservation. and (3) Thin-line detail preservation. The new filter is related to Hodges-Lehman D filter, which is the median of averages of symmetrically placed order statistics. Though it has robust smoothing efficiency, D filter cannot preserve edges or thin-line details. It is shown in this paper that by incorporating a subsampling scheme derived in this paper with the robust D filtering process, the edges as well as the thin-line details can be preserved. The new filter computes its output as the median of weighted averages, instead of plain averages of symmetrically placed order statistics. One particular weighting scheme is considered in details for experiments. The experimental and comparison results are included verifying the useful properties of the proposed filter. To carry out the comparison experiments some new measures for edge and detail preservation are also proposed in the paper.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.