Halit Çetiner, Bayram Cetişli, İbrahim Çetiner

Gerçek Zamanlı Kimlik Numarası Tanıma

In this work, calling of person informations from database as real time and the Republic of Turkey (TC) identification numbers's detection with camera in a very short time are aimed. Presently, inquiry and verification of TC identification is made to execution works of many public and private institutions. The writing of these numbers with eleven digits may not be quickly and accurately for each time. At this point, the correct recognition of identification information is proposed automatically on received identification images from camera by using image processing techniques.

Anahtar Kelimeler:

Optik karakter tanıma, gerçek zamanlı, Hough dönüşümü, dalgacık dönüşümü, yapay sinir ağları.

Real Time Identification Number Recognition

Keywords:

Optical character recognition, real time, Hough transformation, wavelet transformation, artifical neural network.,

PDF

___

Avcı, A. Wavelet dönüşümü ile doku öznitelikleri çıkarılan kullanılarak bölütlenmesi. KTÜ, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 66s., 2006, Trabzon.
Alshebeili, S. A., Nabawib, A. A. F., Mahmoud, S. A. Arabic character recognition using 1-D slices of the character spectrum. Signal Processing V. 56, pp. 59- , 1997.
Chim, Y. C., Kassim, A., Ibrahim, Y. Character recognition using statistical moments. Image and Vision Computing V. 17, pp. 299-307, 1999, Singapore.
Demir, Ö. EEG dalgalarının dalgacık dönüşümü ile değerlendirilmesi. Dumlupınar Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 56s., 2008, Kütahya.
Demuth, B. H., Hagan, M., Beale, M. H. Neural network toolbox user's guide. Mathworks, pp. 404. Elmas, Ç., 2011. Yapay zeka uygulamaları. Seçkin
Yayıncılık, 424s., 2011, Ankara.
Küçük, M., Ağıralioğlu, N. Dalgacık dönüşüm tekniği kullanılarak hidrolojik akım serilerinin modellenmesi. İTÜ dergisi/d mühendislik, 5(2), 69-80s., 2006, İstanbul.
Lee, J. J., Lee, S. M., Kim, I. Y., Min, H. K., Hong, H. S. Comparison between short time fourier and wavelet transform for feature extraction of heart sound. IEEE Tencon, V. 102, pp. 18-55, 1999.
Mallat, S., G. A theory for multi resolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Anal. and Mach. Intell.,11(7), pp. 674-693, 1989.
Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J. M. Wavelet toolbox user’s guide. Mathworks, 1997.
Percival, D. B., Walden, A. T. Wavelet methods for time series anal.. Cambridge University Press, pp. 569, Rao, R. M., Bopardikar, A. S. Wavelet transforms: Introduction to theory and applications. Addison- Wesley, Massachusetts, pp. 336, 1998.
Strang, G., Nguyen, T. Wavelets and Filter Banks. pp. , 1997, USA.
Sarlashkar, M., Bodruzzaman ,M., Malkani, M. J. Feature Extraction Using Wavelet Transform For Neural Network Based Image Classification. pp. 412-416, 1998.
Daubecheis, I. Orthonormal bases of compactly supported wavelets. Pure appl. Match., vol. XLI. PP.906-996, 1998.
Şekil 8.2 Görüntülerin deneysel tanıma sonuçları