Arabic handwritten digit recognition

International Journal on Document Analysis and Recognition

Volumn 11, Issue 3, 2008, Pages 127-141

  Abdleazeem, Sherif ^a   El Sherif, Ezzat ^a  

^a AMERICAN UNIVERSITY IN CAIRO   (Egypt)

Author keywords

Arabic digits; Benchmark; Classifiers; Feature extraction; Indian digits; Two stage

Indexed keywords

CLASSIFIERS; LEARNING SYSTEMS;

ARABIC DIGITS; BENCHMARK; CLASSIFICATION SYSTEMS; CLASSIFICATION TIMES; EXTRACTION TECHNIQUES; FUTURE WORKS; HANDWRITTEN DIGIT RECOGNITIONS; HIGH ACCURACIES; INDIAN DIGITS; RECOGNITION TIMES; TWO-STAGE;

FEATURE EXTRACTION;

EID: 57349134330     PISSN: 14332833     EISSN: 14332825     Source Type: Journal    
DOI: 10.1007/s10032-008-0073-5     Document Type: Article

References (31)

1. LeCun Y., Bottou L., Bengio Y., Haffner P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278-2324 (1998)
2. Dong, J.: Comparison of algorithms for handwritten numeral recognition. Technical report, CENPARMI, Concordia University, 1999
3. Liu C.-L., Nakashima K., Sako H., Fujisawa H.: Handwritten digit recognition: Benchmarking of state-of-the-art techniques. Pattern Recognit. 36, 2271-2285 (2003)
4. Zhang, P., Bui, T., Suen, C.Y.: Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals. Proc. 8th ICDAR, pp. 136-140, 2005
5. Teow L., Loe K.: Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognit. 40(6), 1816-1824 (2007)
6. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI, vol. 24, no. 24, 2002
7. Lauera F., Suen C., Blocha G.: A trainable feature extractor for handwritten digit recognition. Pattern Recognit. 36, 2271-2285 (2003)
8. Gorgevik, D., Cakmakov, D.: An efficient three-stage classifier for handwritten digit recognition. Proc. 17th ICPR, pp. 1051-4651 (2004)
9. Al-Omari F., Al-Jarrah O.: Handwritten Indian numerals recognition system using probabilistic neural networks. Adv. Eng. Inform. 18(1), 9-16 (2004)
10. Said, F., Yacoub, R., Suen, C.: Recognition of English and Arabic numerals using a dynamic number of hidden neurons. Proc. 5th ICDAR, pp. 237-240, 1999
11. El-Sherif, E., Abdelazeem, S.: A two-stage system for Arabic handwritten digit recognition tested on a new large database. International Conference on Artificial Intelligence and Pattern Recognition (AIPR-07), Orlando, FL, USA, July 2007, pp. 237-242
12. Mozaffari, S., Faez, K., Ziaratban, M.: Structural decomposition and statistical description of Farsi/Arabic handwritten numeric characters. Proc. 8th ICDAR, pp. 237-241, 2005
13. Soltanzadeh H., Rahmati M.: Recognition of Persian handwritten digits using image profiles of multiple orientations. Pattern Recognit. Lett. 25(14), 1569-1576 (2004)
14. Duda R., Hart P., Strok D.: Pattern Recognition, 2nd edn. Wiley, New York (2000)
15. Webb A.: Staistical Pattern Recognition, 2nd edn. Wiley, New York (2002)
16. Furnkranz, J.: Round robin classification. J. Mach. Learn. Res., vol. 2, pp. 721-747, 2002
17. Friedman, J.: Another approach to polychotomous classification. Technical Report, Stanford University, 1996
18. Bennett K.P., Mangasarian O.L.: Multicategory discrimination via linear programming. Optim. Methods Softw. 3, 27-39 (1994)
19. Cortes C., Vapnik V.: Support vector networks. Mach. Learn. 20(3), 273-297 (1995)
20. Gonzales R.C., Woods R.E.: Digital image processing, 2nd edn. Addison-Wesley, Reading, MA, USA (2002)
21. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognit., vol. 37, 2004
22. Zhang, P., Bui, T., Suen, C.: Hybrid feature extraction and feature selection for improving recognition accuracy of handwritten numerals. Proc. 8th ICDAR, 2005
23. Trier O.D., Jain A.K., Taxt T.: Feature extraction methods for character recognition - a survey. Pattern Recognit. 29(4), 641-662 (1996)
24. Hanmandlu, M., Kumar, H., Mohan, K.: Neural based handwritten character Recognition. Proc 5th, ICDAR, p. 241, 1999
25. de Oliveira, J., de Carvalho, J., Freitas, C., Sabourin, R.: Feature sets evaluation for handwritten word recognition using a baseline system. Proc. 8th IWFHR, pp. 446-451, 2002
26. Hsu C., Lin C.: A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 1 (13), 415-425 (2002)
27. Kaynak, C., Alpaydin, E.: MultiStage cascading of multiple classifiers: one man's noise is another man's data. Proc. 17th ICML, pp. 455-462, (2000)
28. Alpaydin, E., Kaynak, C., Alimoglu, F.: Cascading multiple classifiers and representations for optical and pen-based handwritten digit recognition. Proc. 7th IWFHR, pp. 453-462, 2000
29. Price, D., Knerr, S., Personnaz, L., Dreyfus, G.: Pairwise neural network classifiers with probabilistic outputs. NIPS, pp. 1109-1116, 1995
30. Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola, A., Bartlett, P., Scholkopf, B., Schuurmans, D. (eds.) Advances in Large Margin Classifiers. Cambridge, MA, pp. 61-74, 2000
31. Davies E.R.: Machine Vision: Theory, Algorithms, Practicalities. Morgan Kaufmann Publishers Inc., San Francisco, CA (2004)