Name
Abstract | ||
|---|---|---|
In this paper, a machine learning approach for classifying handwritten Arabic word is proposed, which includes three stages including preprocessing, feature extraction and classification. Firstly, words are segmented from inputted scripts and also normalized in size. Secondly, three different feature extraction methods are applied to each segmented word namely the discrete cosine transform (DCT), moment invariants, and absolute mean value of overlapping blocks. Finally, theses features are utilized to train a neural network for classification. This approach has been tested using the IFN/ENIT database which consists of 32492 Arabic words. The proposed approach gives a good accuracy when compared with other methods. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CW.2009.62 | Bradford |
Keywords | Field | DocType |
absolute mean value,segmented word,theses feature,handwritten arabic word,feature extraction,different feature extraction method,machine learning approach,enit database,discrete cosine,arabic word,classifying offline handwritten arabic,databases,artificial neural networks,word segmentation,machine learning,learning artificial intelligence,handwriting recognition,dct,neural network,discrete cosine transform,image segmentation,word recognition | Normalization (statistics),Pattern recognition,Computer science,Discrete cosine transform,Handwriting recognition,Image segmentation,Feature extraction,Text segmentation,Preprocessor,Artificial intelligence,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3791-7 | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jawad H AlKhateeb | 1 | 45 | 2.73 |
| Jinchang Ren | 2 | 1144 | 88.54 |
| Jianmin Jiang | 3 | 985 | 81.39 |
| Stan Ipson | 4 | 12 | 2.03 |