期刊名称：Journal of Emerging Technologies in Web Intelligence
印刷版ISSN：1798-0461
出版年度：2014
卷号：6
期号：1
页码：89-93
DOI：10.4304/jetwi.6.1.89-93
语种：English
出版社：Academy Publisher
摘要：In this paper, we are focused on characters recognition, for this we present a comparison between the Krawtchouk Invariant Moment (KIM) and the Pseudo Zernike Invariant Moment (PZIM) for the recognition of printed Arabic characters (translated, rotated and contaminated by noise). In the preprocessing phase, we use the thresholding technique, and in the learning-classification phases, we use the supports vectors machines (SVM).The simulation results demonstrates that the KIM method gives more significant results that the PZIM for each Arabic character.
