In manufacturing, drawings containing proprietary data, must be edited if the decision is made to have the parts produced by an external subcontractor. Filtering of images in addition to text is becoming essential, since modern computing has greatly facilitated the use of information in image form. We believe that this will soon become relevant to electronic medical information. We have developed novel means of recognizing features in images, specifically in linking perceptual factors to parameters in wavelet-based analysis of images. We have experimented with a number of tools, mostly based on parameterized wavelets, that can recognize crucial information, such as text in images, and submit it to the content checking rules our base TIHI system provides.
Initial results are very promising.
In summary, the image filtering that we propose will rely primarily on wavelet technology. Work has been completed at Stanford that demonstrates the capability of indexing and retrieving images by wavelet transform analysis. The wavelet approach has been demonstrated to be fast and highly reliable. Its formal basis provides stability in development over more ad-hoc approaches, and has also been easy to transfer among programming languages. We will further develop the existing algorithms focusing on the properties evidenced by text placed within images.