JIP_V3_N3_RP4 Harshmani Nancy Gupta Gurpreet Kaur Journal on Image Processing 2349-6827 3 3 27 33 Degraded Documents, Character Restoration, Neuro-Fuzzy, BPNN Ancient documents may contain the valuable information of our historical past, which might be available in the printed or handwritten form. The text preservation of these antique documents has a vital significance for future generation and references. But due to several degradation factors such as bleeding through, shadow through, ink bleeding, paper aging, etc., documents are unable to show their contents up to the mark. There are various restoration techniques that may serve this purpose, but due to non-linear and complex nature of the degrading parameters, the existing techniques turn out to be less promising. The aim of study is to investigate the capability of ANN & Fuzzy logic, i.e. 'Neuro-Fuzzy technique' to restore the historical documents from their digital images. In the proposed technique, the Back- Propagation Neural Network (BPNN) is trained to cope with different degrading factors and Fuzzy rules are used to further suppress leftover spurious pixels. The output results of the proposed technique on different degraded document images are presented and compared with various existing techniques, viz. Otsu, Sauvola, Wolf, Niblack, Bernsen and Maximum entropy. The comparative results show the superiority of the proposed technique, which outperforms all other comparative techniques by providing visually better output images. July - September 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=8148