Abstract: A method and apparatus for preventing or inhibiting effective reproduction of documents such as currency, checks, stock certificates, and any other printed document including a pre-defined security mark printed therein. The subject method and apparatus operate to effect a multi-step review of all digital image data acquired from a printed document to be reproduced for purposes of locating any potential security marks and further examining same for purposes of positively identifying a potential security mark as an actual security mark. If a mark is located and verified to be an authentic security mark, effective reproduction of the printed document will not be permitted and/or other security measures will be taken. A pretest of a mark consists of deleting the fine line patterns that may be printed on top of the mark by smoothing the data in an averaging process, and then determining the difference between the density of the darkest and lightest pixels in the area.

Abstract: This algorithm is a method for resizing and resampling an image in one step. In the vertical, process or slow scan, direction, from the resampling ratio and from the image size variation, an algorithm can compute where, on the page, the scan lines may be deleted. For a numerical example, if the input is 400 pixels per unit time, resampling of four to one would yield one scan line every {fraction (1/100)} of a unit. If, in addition, the image has to be reduced by 5%, then the final output would be one scan line every {fraction (1/95)}th of a time unit. This algorithm accomplishes this result by using a running sum of error terms. Simply stated, if it is known that the previous output scan was output at exactly the right time, the error is zero, but if the scan was output too soon or too late, there will be an error term. For each new scan that is now output, {fraction (1/400)}th of a time unit is added to the error term.

Abstract: An anti-counterfeit currency detection method is disclosed wherein local edge information is utilized for accurately detecting lines and curves of legitimate notes. The method can more accurately determine the location and orientation of a pattern and thus provides more reliable currency detection. A detector is trained off-line with example notes resulting in a stored template generated by recording a test pattern similar to a pattern to be tested; anchor lines are identified within said template which are further represented in subsequent test patterns. The template is rotated and shifted before matching it to the test pattern so that anchor lines align with long lines detected within the test pattern. The template and test pattern are then compared to determine whether there is a match.