Name
Abstract | ||
|---|---|---|
This paper presents a new method called density weight and zigzag sequence to recognize printed Arabic names. This technique was performed on two steps, the first aims to reduce matrix size of 96x96 into 12x12 using density weight techniques, in the second step the last matrix 12x12 was used to extract 144 sequences following path zigzag technique. 144 features found are used for representing each name in data set. This proposed technique was tested on Morocco town and village names using KNN with consensus rule and SVM classifiers. The perfect score was obtained with KNN k=9 and SVM linear kernel. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4018/jitr.2014100101 | Journal of Information Technology Research |
Keywords | Field | DocType |
Density Weight, KNN Consensus, Linear Kernel, Moroccan Town and Village, Printed Arabic Names, SVM, Zigzag Sequences | Kernel (linear algebra),Data mining,Pattern recognition,Arabic,Computer science,Matrix (mathematics),Support vector machine,Speech recognition,Artificial intelligence,Zigzag | Journal |
Volume | Issue | ISSN |
7 | 4 | 1938-7857 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Said Nouri | 1 | 0 | 0.34 |
| M. Fakir | 2 | 5 | 4.14 |