In the last two years we have been testing FISH in the Netherlands police practice. FISH is a system for automatic recognition of handwriting originally developed by the BKA in Germany. Some of the most important classification algorithms that are used compute a feature vector from a digital binary image that shows handwriting without background patterns. This image is currently obtained in three steps: scanning the document (200 dpi, 64 bits), binary segmentation using a interactively selected threshold value, and interactive removal of background patterns. However, the resulting image sometimes shows black structures or white spaces that are not visible in the original document. In this study we investigated the use of color transformation for obtaining better binary images from digital 3 by 8 bit RGB color images. In each distinctive area of the image, the ink lines, background, and background patterns, a number of pixels are selected interactively and their RGB-values are sorted. Analysis of their distributions showed that separation of ink line and background using R, G and B threshold values can often be improved by performing, prior to the segmentation, a 3D rotation of the RGB-values of all pixels. Methods and results are presented.

Date Published: 1 September 1995
PDF: 11 pages
Proc. SPIE 2567, Investigative and Trial Image Processing, (1 September 1995); doi: 10.1117/12.218477

Published in SPIE Proceedings Vol. 2567:
Investigative and Trial Image Processing
Leonid I. Rudin; Simon K. Bramble, Editor(s)