Research Article
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Year 2021, Volume: 25 Issue: 1, 65 - 71, 01.02.2021
https://doi.org/10.16984/saufenbilder.801684

Abstract

References

  • [1] Sekerci, M., “Birlesik ve egik Turkce el yazisi tanima sistemi,” Trakya University, Master’s Thesis, 2007.
  • [2] Yilmaz, B., “Ogrenme guclugu ceken cocuklar icin el yazisi tanima ile ogrenmeyi kolaylastirici bir mobil ogrenme uygulamasi tasarimi,” Maltepe University, Master’s Thesis, 2014.
  • [3] Dagdeviren, E., “El yazisi rakam tanima icin destek vektor makinelerinin ve yapay sinir aglarinin karsilastirmasi,” Istanbul University, Master’s Thesis, 2013.
  • [4] Assegie, T. A., Nair, P. S., “Handwritten digits recognition with decision tree classification: a machine learning approach,” International Journal of Electrical and Computer Engineering (IJECE), Indonesia, 446-4451, 2019.
  • [5] Arica, N., Vural, Y., F.T., “An overview of character recognition focused on offline handwriting,” IEE Transactions on Systems Man and Cybernetics, 2001.
  • [6] MacKenzie, S., Tanaka-Ishii, K., “Text entry systems,” Morgan Kaufmann Publishing, 123-137, 2007.
  • [7] Gunn, S.R., “Support vector machines for classification and regression,” 1998.
  • [8] Safavian, S.R., Landgrebe, D., “A survey of decision tree classifier methodology,” IEE Transactions on Systems Man and Cybernetics, 1991.
  • [9] Géron, A., “Hands-On machine learning with Scikit-Learn and TensorFlow concepts,” O’Reilly Publishing, 2017.
  • [10] Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R. Wu, A.Y., “An optimal algorithm for approximate nearest neighbor searching in fixed dimension,” Journal of the ACM, 2017.
  • [11] Kirmizigul Caliskan, S., Sogukpinar, I., KxKNN : “K-ortalama ve K-en yakin komsu yontemleri ile aglarda nufuz tespiti,” Electronic Journal of Vocational Colleges (Ejovoc) , Kırklareli , 2015.
  • [12] Gunes, A., Yigit, T., “Hizlandirilmis destek vektor makineleri ile el yazisi rakamlarin taninmasi,” 20th Signal Processing and Communications Applications Conference, Mugla, 2012.
  • [13] Liu, C., Fujisawa, H., “Handwritten digit recognition: Benchmarking of state-of-the-art techniques,” Pattern Recognition, 2003.
  • [14] Fialoke, S., “Predicting Digits from their handwritten images, (16.10.2020), http://suruchifialoke.com/2017-06-15-predicting-digits_tensorflow.

Handwritten Digit Recognition Using Machine Learning

Year 2021, Volume: 25 Issue: 1, 65 - 71, 01.02.2021
https://doi.org/10.16984/saufenbilder.801684

Abstract

Technology is getting more and more involved in our lives, and so are algorithms. These algorithms speed up work and reduce workload. Especially machine learning algorithms are improving day by day by imitating human behaviours. Handwriting recognition systems are also stand out on this field. In this study, handwriting digit recognition process has been done with algorithms having different working methods. These algorithms are Support Vector Machine (SVM), Decision Tree, Random Forest, Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN) and K- Means Algorithm. The working logic of the handwriting digit recognition process was examined, and the efficiency of different algorithms on the same database was measured. A report was presented by making comparisons on the accuracy.

References

  • [1] Sekerci, M., “Birlesik ve egik Turkce el yazisi tanima sistemi,” Trakya University, Master’s Thesis, 2007.
  • [2] Yilmaz, B., “Ogrenme guclugu ceken cocuklar icin el yazisi tanima ile ogrenmeyi kolaylastirici bir mobil ogrenme uygulamasi tasarimi,” Maltepe University, Master’s Thesis, 2014.
  • [3] Dagdeviren, E., “El yazisi rakam tanima icin destek vektor makinelerinin ve yapay sinir aglarinin karsilastirmasi,” Istanbul University, Master’s Thesis, 2013.
  • [4] Assegie, T. A., Nair, P. S., “Handwritten digits recognition with decision tree classification: a machine learning approach,” International Journal of Electrical and Computer Engineering (IJECE), Indonesia, 446-4451, 2019.
  • [5] Arica, N., Vural, Y., F.T., “An overview of character recognition focused on offline handwriting,” IEE Transactions on Systems Man and Cybernetics, 2001.
  • [6] MacKenzie, S., Tanaka-Ishii, K., “Text entry systems,” Morgan Kaufmann Publishing, 123-137, 2007.
  • [7] Gunn, S.R., “Support vector machines for classification and regression,” 1998.
  • [8] Safavian, S.R., Landgrebe, D., “A survey of decision tree classifier methodology,” IEE Transactions on Systems Man and Cybernetics, 1991.
  • [9] Géron, A., “Hands-On machine learning with Scikit-Learn and TensorFlow concepts,” O’Reilly Publishing, 2017.
  • [10] Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R. Wu, A.Y., “An optimal algorithm for approximate nearest neighbor searching in fixed dimension,” Journal of the ACM, 2017.
  • [11] Kirmizigul Caliskan, S., Sogukpinar, I., KxKNN : “K-ortalama ve K-en yakin komsu yontemleri ile aglarda nufuz tespiti,” Electronic Journal of Vocational Colleges (Ejovoc) , Kırklareli , 2015.
  • [12] Gunes, A., Yigit, T., “Hizlandirilmis destek vektor makineleri ile el yazisi rakamlarin taninmasi,” 20th Signal Processing and Communications Applications Conference, Mugla, 2012.
  • [13] Liu, C., Fujisawa, H., “Handwritten digit recognition: Benchmarking of state-of-the-art techniques,” Pattern Recognition, 2003.
  • [14] Fialoke, S., “Predicting Digits from their handwritten images, (16.10.2020), http://suruchifialoke.com/2017-06-15-predicting-digits_tensorflow.
There are 14 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Rabia Karakaya 0000-0003-2704-3708

Serap Kazan This is me 0000-0002-3682-0831

Publication Date February 1, 2021
Submission Date September 29, 2020
Acceptance Date October 30, 2020
Published in Issue Year 2021 Volume: 25 Issue: 1

Cite

APA Karakaya, R., & Kazan, S. (2021). Handwritten Digit Recognition Using Machine Learning. Sakarya University Journal of Science, 25(1), 65-71. https://doi.org/10.16984/saufenbilder.801684
AMA Karakaya R, Kazan S. Handwritten Digit Recognition Using Machine Learning. SAUJS. February 2021;25(1):65-71. doi:10.16984/saufenbilder.801684
Chicago Karakaya, Rabia, and Serap Kazan. “Handwritten Digit Recognition Using Machine Learning”. Sakarya University Journal of Science 25, no. 1 (February 2021): 65-71. https://doi.org/10.16984/saufenbilder.801684.
EndNote Karakaya R, Kazan S (February 1, 2021) Handwritten Digit Recognition Using Machine Learning. Sakarya University Journal of Science 25 1 65–71.
IEEE R. Karakaya and S. Kazan, “Handwritten Digit Recognition Using Machine Learning”, SAUJS, vol. 25, no. 1, pp. 65–71, 2021, doi: 10.16984/saufenbilder.801684.
ISNAD Karakaya, Rabia - Kazan, Serap. “Handwritten Digit Recognition Using Machine Learning”. Sakarya University Journal of Science 25/1 (February 2021), 65-71. https://doi.org/10.16984/saufenbilder.801684.
JAMA Karakaya R, Kazan S. Handwritten Digit Recognition Using Machine Learning. SAUJS. 2021;25:65–71.
MLA Karakaya, Rabia and Serap Kazan. “Handwritten Digit Recognition Using Machine Learning”. Sakarya University Journal of Science, vol. 25, no. 1, 2021, pp. 65-71, doi:10.16984/saufenbilder.801684.
Vancouver Karakaya R, Kazan S. Handwritten Digit Recognition Using Machine Learning. SAUJS. 2021;25(1):65-71.