Title :
A new thinning algorithm for Arabic characters using self-organizing neural network
Author :
Altuwaijri, Majid ; Bayoumi, Magdy
Author_Institution :
Center for Adv. Comput. Studies, Southwestern Louisiana Univ., Lafayette, LA, USA
fDate :
30 Apr-3 May 1995
Abstract :
In this paper, we propose a new thinning algorithm based on clustering the data image. We employ the ART2 network which is a self-organizing neural network for the clustering of Arabic characters. The skeleton is generated by plotting the cluster centers and connecting adjacent clusters by straight lines. This algorithm produces skeletons which are superior to the outputs of the conventional algorithms. It achieves a higher data reduction efficiency and much simpler skeletons with less noise spurs. Moreover, to make the algorithm appropriate for real-time applications, an optimization technique is developed to reduce the time complexity of the algorithm. Nevertheless, the algorithm is not limited to Arabic characters, it can, also, be used to skeletonize characters of other languages
Keywords :
ART neural nets; character recognition; computational complexity; self-organising feature maps; ART2 network; Arabic characters; adjacent clusters; cluster centers; data image clustering; data reduction efficiency; real-time applications; self-organizing neural network; skeletons; thinning algorithm; time complexity; Character recognition; Clustering algorithms; Computer networks; Image processing; Inspection; Joining processes; Neural networks; Noise reduction; Printed circuits; Skeleton;
Conference_Titel :
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2570-2
DOI :
10.1109/ISCAS.1995.523769