The microscopic details of printing often are unnoticed by humans, but can make differences that affect machine recognition of printed text. Models of the defects introduced into images by printing can be used to improve machine recognition. A probabilistic model used to generate images showing toner placement bears similarities to actual printed images. An equation derived for the average coverage of paper by toner particles having probabilistic placement is developed using geometric probability. Simulations show that averages of ‘printed images’ do have the same average coverage as the derived average coverage equations.
This document was originally published by CSREA/WORLDCOMP Press in WorldComp'14, July 21-14, Las Vegas, Nevada, USA, The 2014 World Congress in Computer Science and Computer Engineering and Applied Computing. Copyright restrictions may apply.
Norris, Margaret and Barney Smith, Elisa H.. (2004). "Printer Modeling for Document Imaging". Proceedings of the 2004 International Conference on Imaging Science, Systems, and Technology (CISST'04), 1-7.