Persian Handwritten Recognition Using Centroid Sequence Freeman Chain Code (CSFCC) Feature and Enhanced Classification

Document Type : Persian Original Article

Authors

Computer Dept., Engineering Faculty, Razi University, Kermanshah, Iran

Abstract

In this paper, a new method is proposed for offline handwritten Persian words recognition. The proposed method introduces the Centroid Sequence Freeman Chain Code (CSFCC) as a new and powerful feature along with the use of morphological features and an optimize support vector machine (SVM) classifier. A conbination of particle swarm optimization (PSO) and gravitational search algorithm (GSA), abbreviated to PSOGSA, has been employed to optimaze the SVM classifier. In the proposed method, all the connected components of a word are detected and combined with each other. For this purpose, a pictorial dictionary of asymptomatic subwords has been made. In addition, a database has been created to include the positions of asymptomatic subwords in order to narrow down the search space and increase the speed and improve the recognition accuracy. Based on the position of a subword in a word, it is more likely to make the right decision and detect the subword, accurately. The proposed method was implemented on the Iranshahr Database, containing nearly 17000 images of handwritten names of 503 cities of Iran. The resultant recognition accuracy is 89% in the expriments, which shows the capability of the proposed method and improving the results, compared to the other well-known methods.

Keywords

References

[1]    A. A Aburas and S. M. Rehie, “OffLine Omni-Style Handwriting Arabic Character Recognition System Based on Wavelet Compression”, Arab Research Institute in Sciences & Engineering, vol.3, No.4, pp. 123-135, 2007.
[2]    S. Nasrollahi and A. Ebrahimi, “Printed Persian Subword Recognition Using Wavelet Packet Descriptors”, Journal of Engineering, Vol.2013, 2013.
]3[     امید هاشمی قوچانی و علیرضا سیدین، " شناسایی برون‌خط کلمات دست‌نویس فارسی با تاکید بر تشخیص نام چند شهر"، هفتمین کنفرانس ماشین بینایی و پردازش تصویر ایران، آبان 1390.
[4]     N. Arica and T. Yamin-Vural Fatos, “An overview of character recognition based focused on offline handwriting”, IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol.31, No.2, 2001.
 [5]    M. Zand, A. N. Nilchi and S. A. Monadjemi, “Recognition-based Segmentation in Persian Character Recognition”, World Academy of Scienci, Engineering and Technology, Vol.38, pp. 183-187, 2008.
[6]     M. F. Y. Ghadikolaie, E. Kabir and F. Razzazi, “Sub-word Based Offline Handwritten Farsi Word Recognition Using Recurrent Neural Network”, ETRI Journal, Vol.38, N.4, pp. 703–713, 2016.
[7]     M. Omidyeganeh, K. Nayeb, R. Azmi and A. Javadtalab, “A new segmentation technique for multi font farsi/arabic texts”, IEEE International Conference on Acoustics, Speech, and Signal Processing ,  vol.2, pp. 757–760, 2005.
[8]     M. Omidyeganeh, R. Azmi, K. Nayebi, and A. Javadtalab, “A new method to improve multi font Farsi/arabic character segmentation results: using extra classes of some character combinations”, International Conference on Multimedia Modeling, pp. 670–679, 2007.
[9]     G. Katiyar  and S. Mehfuz, “A hybrid recognition system for offline handwritten characters” SpringerPlus, Vol. 5, No.1, 2016.
[10]   H. Soltanzadeh and M. Rahmati, “Recognition of Persian handwritten digit using image profiles of multiple orientations”,   Pattern Recognition Letters, Vol.25, No.14, pp. 1569-1576, 2004.
[11]   G. Vamvakas, B. Gatos and S.J. Perantonis, “Handwritten character recognition through two stage foreground sub-sampling”, Pattern Recognition, Vol.43, No.8, pp. 2807-2816, 2010.
[12]   K. Bagheri Noaparast and A. Broumandnia, “Persian handwritten word recognition using Zernike and fourier-mellin moments”, 5th International Conference Sciences of Electronic, Technologies of Information and Telecommunications, 2009.
[13]   H. Karimi, A. Esfahanimehr, M. Mosleh, F. M. Jadval ghadam, S. Salehpour, and O. Medhati, “Persian handwritten digit recognition using ensemble classifiers”, Procedia Computer Science , Vol.73, pp. 416-425, 2015.
[14]   P. Zhang, T. D. Bui, and C. Y. Suen, “A novel cascade ensemble classifier system with a high recognition performance on handwritten digits”, Pattern Recognition, Vol.40, No.12, pp. 3415-3429, 2007.
[15]   A. Mowlaei, K. Faez, and A. T. Haghighat, “Feature Extraction with Wavelet Transform for Recognition of Isolated Handwritten Farsi /Arabic Characters and Numerals”, Digital Signal Processing, Vol.2, pp. 923-926, 2002.
[16]   R. Sheikhpour, M. A. Sarram, S. Gharaghani and M. A. Z. Chahooki, “A Survey on semi-supervised feature selection methods”, Pattern Recognition, Vol.64, pp. 141-158, 2017.
[17]   R. Azmi, B. Pishgoo, N. Norozi, M. Koohzadi and F. Baesi, “A hybrid GA and SA algorithms for feature selection in recognition of handprinted Farsi characters”, IEEE International Conference on Intelligent Computing and Intelligent Systems, Vol.3, pp. 384-387, 2010.
 [18]  N. Shanthi, and K. Duraiswamy, “A novel SVM-based handwritten Tamil character recognition system”, Pattern Analysis & Applications, Vol.13, No. 2, pp. 173-180, 2009.
[19]   C. J. Burges, “A tutorial on support vector machines for pattern recognition”, Data Mining Knowledge Discovery, Vol.2, No.2, pp. 121-167, 1998.
[20]   B.E. Boser, I.M. Guyon, and V.N. Vapnik, “A training algorithm for optimal margin classifiers”, In Proceedings of the fifth annual workshop on Computational learning theory,  1992.
[21]   Y. Wu, F. Yin  and C. Liu, “Improving handwritten Chinese text recognition using neural network language models and convolutional neural network shape models”, Pattern Recognition, Vol. 65, pp. 251-264, 2017.
[22]   M. Elleuch, R. Maalej and M. Kherallah, "A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition", Procedia Computer Science, vol.80, pp. 1712-1723, 2016.
[23]   M .Dehghan and K.Faez, “Handwritten Farsi Character Recognition Using Evolutionary Fuzzy Clustering”, Signal Processing Conference, 1998.
[24]   J. H. AlKhateeb, J. Jiang, J. Ren, F. Khelifi , S. S. Ipson, “Multiclass Classification of Unconstrained Handwritten Arabic Words Using Machine Learning Approaches”, The Open Signal Processing Journal, vol.2, No.1, pp. 21–28, 2009.
[25]   A. Ebrahimi and E. Kabir, “A pictorial dictionary for printed farsi subwords”, Pattern Recognition Letters, Vol.29, No.5, pp. 656-663, 2008.
]26[   مسعود فرکی، ومازیار پالهنگ، " بازشناسی برخط حروف فارسی بر پایه مدل مخفی مارکوف"، مجله مهندسی برق دانشگاه تبریز، جلد40، شماره1، صفحه 34-23، 1389.
]27[   محمد نحوی، مهدی رفیعی، رضا ابراهیم پور و  احسان اله کبیر، "ترکیب طبقه‌بندهای دوکلاسی برای بازشناسی ارقام دست‌نویس فارسی"، شانزدهمین کنفرانس مهندسی برق ایران، تهران، دانشگاه تربیت مدرس،۱۳۸۷.
[28]   Z. Tamen, H. Drias and D. Boughaci, ”An efficient multiple classifier system for Arabic handwritten words recognition”, Pattern Recognition Letters, vol. 93, pp. 123-132, 2017.
[29]   J. Kennedy and R. Eberhart, “Swarm Intelligence”, Morgan Kaufmann Publishers, 2001.
[30]   S. Mirjalili, S. Z. M. Hashim and H. M.  Sardroudi, “Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm”, Applied Mathematics and Computation, Vol.218, No.22, pp. 11125-11137, 2012.
[31]   E. Rashedi, H. Nazemabadi-poor, and S. Saryazdi, “GSA: A Gravitational Search Algorithm”, Information Sciences, Vol.179, pp. 2232–2248, 2009.
[32]   A. Boukharouba and A. Bennia,”Novel feature extraction technique for the recognition of handwritten digits”, Applied Computing and Informatics, Vol.13, No.1, Pages 19-26, 2017.
[33]        R. C.  Gonzalez, and R. E. Woods, “Digital image processing”, Addison-Wesley, 2nd edition, 2002.
Journal of Soft Computing and Information Technology
Volume 7, Issue 2 - Serial Number 2
Fall & Winter
January 2019
Pages 43-57

Files

Share

How to cite

Statistics