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Off-line Arabic/Farsi handwritten word recognition using RBF neural network and genetic algorithm

Bahmani, Z ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/ICICISYS.2010.5658635
  3. Publisher: 2010
  4. Abstract:
  5. In this paper an off-line ArabiclFarsi handwritten recognition Algorithm on a subset of Farsi name is proposed. In this system, There is no sub-word segmentation phase. Script database includes 3300 images of 30 Farsi common names. The features are wavelet coefficients extracted from smoothed word image profiles in four standard directions. The Centers of competitive layer of RBF neural network have been determined by combining GA and K-Means clustering algorithm. Weights of supervised layer has been trained by using LMS rule and the distances of feature vector of each sample to the centre of RBF network have been computed based on warping function. Experimental results show advantages of this method in field of handwriting recognition
  6. Keywords:
  7. Farsi handwritten recognition ; Genetic algorithm ; RBF neural network and wavelet transform ; Feature vectors ; Handwriting recognition ; Handwritten recognition ; Handwritten word recognition ; In-field ; k-Means algorithm ; K-Means clustering algorithm ; RBF Network ; RBF Neural Network ; Warping function ; Wavelet coefficients ; Word images ; Word segmentation ; Character recognition ; Computational linguistics ; Genetic algorithms ; Intelligent computing ; Intelligent systems ; Neural networks ; Radial basis function networks ; Wavelet transforms ; Clustering algorithms
  8. Source: Proceedings - 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010, 29 October 2010 through 31 October 2010, Xiamen ; Volume 3 , 2010 , Pages 352-357 ; 9781424465835 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5658635/?reload=true