In Handwriting character recognision can be used to seek texts in big documents, take notes on tablet or decide whether or not internet user is a human or a computer in terms of Web security. In this study, a handwriting recognition system is studied by using fuzzy rules. The system includes 4 parts, namely image processing, feature extraction, fuzzification of the inputs, and defuzzification. In the first stage, image processing based on morphological operations are used to perform the handwriting recognisition under the same conditions. The feature extraction process is employed to find the total number of white pixels in each column. Then these pixel numbers are assigned to arrays. The next step is to find the local maximum and minimum values by considering this arrays as an increasing-decreasing mathematical function. Therefore, it is observed that the handwritten letters of these values are divided into various groups. In the next operation, fuzzy classification membership functions and rule tables of text groups are generated by using extracted feature data. For a better recognition perfromance, the letters group have to be known in order to use image fuzzy logic algorithm. Consequently, this group of letters was succesfully classified with fuzzy logic rules.
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4. Jasim M. K., Al-Saleh A. M., Aljanaby A. (2013). A Fuzzy Based Feature Extraction Approach for Handwritten Characters. International Journal of Computer Science Issues (IJCSI). 10: 208-2015.
5. Gowan W. A. (1995). Optical character recognition using fuzzy logic. Microprocessors and Microsystems, 19: 423-434.
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Year 2017,
Volume: 12 Issue: 2, 71 - 77, 01.10.2017
1. Erdem O. A., Uzun E. (2005). Turkish Times New Roman, Arial, And Handwriting Characters Recognition By Neural Network: Journal of the Faculty of Engineering and Architecture of Gazi University, Ankara, 20: 13-19.
2. Weijie S., Jin X. (2011). Hidden Markov Model with Parameter-Optimized K-Means Clustering for Handwriting Recognition. IEE E2011 International Conference on Internet Computing & Information Services (ICICIS), Hong Kong, 235-438.
3. Prasad, M. M., Sukumar M. (2013). 2D-LDA based online handwritten kannada character recognition. Int. Jl. of Computer Science and Telecommunications 4(1): 14-18.
4. Jasim M. K., Al-Saleh A. M., Aljanaby A. (2013). A Fuzzy Based Feature Extraction Approach for Handwritten Characters. International Journal of Computer Science Issues (IJCSI). 10: 208-2015.
5. Gowan W. A. (1995). Optical character recognition using fuzzy logic. Microprocessors and Microsystems, 19: 423-434.
Vardar, E., Kaplan, K., & Ertunç, H. M. (2017). Handwriting Character Recognision by using Fuzzy Logic. Turkish Journal of Science and Technology, 12(2), 71-77.
AMA
Vardar E, Kaplan K, Ertunç HM. Handwriting Character Recognision by using Fuzzy Logic. TJST. October 2017;12(2):71-77.
Chicago
Vardar, Enes, Kaplan Kaplan, and H. Metin Ertunç. “Handwriting Character Recognision by Using Fuzzy Logic”. Turkish Journal of Science and Technology 12, no. 2 (October 2017): 71-77.
EndNote
Vardar E, Kaplan K, Ertunç HM (October 1, 2017) Handwriting Character Recognision by using Fuzzy Logic. Turkish Journal of Science and Technology 12 2 71–77.
IEEE
E. Vardar, K. Kaplan, and H. M. Ertunç, “Handwriting Character Recognision by using Fuzzy Logic”, TJST, vol. 12, no. 2, pp. 71–77, 2017.
ISNAD
Vardar, Enes et al. “Handwriting Character Recognision by Using Fuzzy Logic”. Turkish Journal of Science and Technology 12/2 (October2017), 71-77.
JAMA
Vardar E, Kaplan K, Ertunç HM. Handwriting Character Recognision by using Fuzzy Logic. TJST. 2017;12:71–77.
MLA
Vardar, Enes et al. “Handwriting Character Recognision by Using Fuzzy Logic”. Turkish Journal of Science and Technology, vol. 12, no. 2, 2017, pp. 71-77.
Vancouver
Vardar E, Kaplan K, Ertunç HM. Handwriting Character Recognision by using Fuzzy Logic. TJST. 2017;12(2):71-7.