Font Recognition of English Letters Based on Distance Profile Features

  • Aveen J. Mohammed Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region - Iraq.
  • Hasan S.M. Al-Khaffaf Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region - Iraq
Keywords: distance profile features, support vector machines, English font recognition, character font classification, optical font recognition

Abstract

This paper presents a system for recognizing English fonts from character images. The distance profile is the feature of choice used in this paper. The system extracts a vector of 106 features and feeds it into a support vector machine (SVM) classifier with a radial basis function (RBF) kernel. The experiment is divided into three phases. In the first phase, the system trains the SVM with different Gamma and C parameters. In the second phase, the validation phase, we validate and select the pair of Gamma and C values that yield the best recognition rates. In the final phase, the testing phase, the images are tested and the recognition rate is reported. Experimental results based on 27,620 characters glyph images from three English fonts show a 94.82% overall recognition rate.

Author Biographies

Aveen J. Mohammed, Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region - Iraq.

Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region, Iraq – (avin.kovli@gmail.com)

Hasan S.M. Al-Khaffaf, Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region - Iraq

Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region, Iraq (hasan.salim@uod.ac).

References

Al-Khaffaf, H. S., & Musa, N. A. (2018). Optical english font recognition in document images using eigenfaces. Revista Innovaciencia, 6(1), 1-11.
Al-Khaffaf, H. S., Shafait, F., Cutter, M. P., & Breuel, T. M. (2012, November). On the performance of Decapod's digital font reconstruction. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) (pp. 649-652). IEEE.
Bharath, V., & Rani, N. S. (2017, June). A font style classification system for English OCR. In 2017 International Conference on Intelligent Computing and Control (I2C2) (pp. 1-5). IEEE.
Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
Bouchut, Q., Appiah, K., Lotfi, A., & Dickinson, P. (2018, June). Ensemble One-vs-One SVM Classifier for Smartphone Accelerometer Activity Recognition. In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 1110-1115). IEEE.
Bui, T., & Collomosse, J. (2015, September). Font finder: Visual recognition of typeface in printed documents. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 3926-3930). IEEE.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
Hajiannezhad, A., & Mozaffari, S. (2012, May). Font recognition using variogram fractal dimension. In 20th Iranian Conference on Electrical Engineering (ICEE2012) (pp. 634-639). IEEE.
Jaiem, F. K., Slimane, F., & Kherallah, M. (2017, February). Arabic font recognition system applied to different text entity level analysis. In 2017 International Conference on Smart, Monitored and Controlled Cities (SM2C) (pp. 36-40). IEEE.
Kanungo, T., Haralick, R. M., Baird, H. S., Stuezle, W., & Madigan, D. (2000). A statistical, nonparametric methodology for document degradation model validation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1209-1223.
Katiyar, G., Katiyar, A., & Mehfuz, S. (2017). Off-line handwritten character recognition system using support vector machine. American Journal of Neural Networks and Applications, 3(2), 22-28.
Kecman, V. (2005). Support vector machines–an introduction. In Support vector machines: theory and applications (pp. 1-47). Springer, Berlin, Heidelberg.
Kowalczyk, A. (2017). Support vector machines succinctly. Syncfusion Inc.
Liu, Y., Wang, R., & Zeng, Y. S. (2007, August). An improvement of one-against-one method for multi-class support vector machine. In 2007 International Conference on Machine Learning and Cybernetics (Vol. 5, pp. 2915-2920). IEEE.
Scholkopf, B., Sung, K. K., Burges, C. J., Girosi, F., Niyogi, P., Poggio, T., & Vapnik, V. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE transactions on Signal Processing, 45(11), 2758-2765.
Senobari, E. M., & Khosravi, H. (2012, October). Farsi font recognition based on combination of wavelet transform and sobel-robert operator features. In 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE) (pp. 29-33). IEEE.
SKLearn Library (2020, February 21). https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html. Accessed on Sep 2019.
Tensmeyer, C., Saunders, D., & Martinez, T. (2017, November). Convolutional neural networks for font classification. In 2017 14th IAPR international conference on document analysis and recognition (ICDAR) (Vol. 1, pp. 985-990). IEEE.
Xu, Y., Zomer, S., & Brereton, R. G. (2006). Support vector machines: a recent method for classification in chemometrics. Critical Reviews in Analytical Chemistry, 36(3-4), 177-188.
Published
2020-06-30
How to Cite
Mohammed, A., & Al-Khaffaf, H. (2020). Font Recognition of English Letters Based on Distance Profile Features. Science Journal of University of Zakho, 8(2), 66-71. https://doi.org/10.25271/sjuoz.2020.8.2.694
Section
Science Journal of University of Zakho