Document Type : Research Paper


1 Computer Engineering Department, College of Engineering, University of Mosul, Mosul, Iraq

2 WiPNET Research Centre, Dept. of Computer and Communication Systems Engineering, Faculty of Engineering, UPM, Selangor, Malaysia


Day by day, machine learning and deep learning reduce the efforts needed by humans in many fields. Handwriting recognition is one such field. In Handwriting Recognition (HWR), a machine can interpret and recognize handwritten input from different sources like papers, touch screens, images, etc. by interpreting it into machine-readable formats. Arab countries often use Arabic digits in addition to English digits. In banks, business applications, etc. This article discusses four methods to recognize Arabic/English handwritten digits which are: random forest (RF), multi-layer perceptrons (MLPs), convolutional neural network (CNN), and CNN-RF. These methods were implemented with the help of the MNIST and MADBase datasets and the results appear that in comparison with the other algorithms, the highest accuracy was obtained by the Convolutional Neural Network (CNN) with a value of 99.11%.


Main Subjects

  1. A. Alan,” Arabic Handwritten Digit Recognition Based on Restricted Boltzmann Machine and Convolutional Neural Networks,” Information, Vol. 8, No.4, pp. 142, 2017.
  2. A. H. Al-Jameel, M. D. Younis, and R. M. Hamed,” Congregational Mosques Classification Using Pattern Recognition Method,” Al-Rafidain Engineering Journal (AREJ), Vol.21, No.6,pp. 71-87, 2013.
  3. C. Kaensar,” A Comparative Study on Handwriting Digit Recognition Classifier Using Neural Network, Support Vector Machine, and K-Nearest Neighbor,” The 9th International Conference on Computing and InformationTechnology(IC2IT2013)vol. 209, p. 155-163, 2013.
  4. S.Ahlawat, A.Choudhary, A.Nayyar, S.Singh, B.Yoon,” Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN),” Sensors, vol.20, pp. 3344, 2020.
  5. V.R.Rudraswamimath, K. Bhavanishankar,” Handwritten Digit Recognition using CNN,” International Journal of Innovative Science and Research Technology, Vol. 4, No. 6, June 2019.
  6. W. Liu, J. Wei and Q. Meng, “Comparisions on KNN, SVM, BP and the CNN for Handwritten Digit Recognition,” 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), 2020, pp. 587-590, doi: 10.1109/AEECA49918.2020.9213482.
  7. N. Altwaijry, I. Al-Turaiki, “Arabic handwriting recognition system using convolutional neural network,” Neural Computing and Applications, vol. 33, pp.2249–2261,2021.
  8. P. Patil, B. Kaur,” Handwritten Digit Recognition Using Various Machine Learning Algorithms and Models,” International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol. 8,No. 4, ISSN: 2347-5552, 2020.
  9. A. V. S. R. Praneeth, L. R. Pappu, G. P. Kumar, A. S. Bhatta, B. R. Chowdary, and G. S. Shishwan. “Hand Written Digit Recognition Using Machine Learning,” International Journal of Research in Engineering, Science and Management, vol. 5, No. 1, pp. 59-62, , Jan. 2022.S
  10. S. Flora, A. Kakkad,” Comparison Study of Handwritten Digit Recognition using Artificial Neural Network and Convolutional Neural Network: A Review,” International Journal of Emerging Technologies and Innovative Research (JETIR), Vol. 6, No.5, ISSN-2349-5162, May 2019.
  11. S. S.Rosyda, T. W. Purboyo,” A Review of Various Handwriting Recognition Methods,” International Journal of Applied Engineering Research, vol.13, ISSN 0973-4562, No. 2, pp. 1155-1164, 2018.
  12. A.Dutt, A.Dutt, “Handwritten Digit Recognition Using Deep Learning,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 6, No.7, ISSN: 2278 – 1323, PP. 990-997, July 2017.
  13. H.N.Abdul, N. N. A.Sjarif, “Handwritten Recognition Using SVM, KNN and Neural Network,” ArXiv abs/1702.00723,2017.
  14. A. Ashiquzzaman, A. K. Tushar, "Handwritten Arabic numeral recognition using deep learning neural networks," 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1-4, 2017, doi: 10.1109/ICIVPR.2017.7890866.
  15. A.Pardamean, D. Yuliana, S. Watmah, S. Hikmawan, S.fenrianto,” Arabic Handwritten Digit Recognition using Convolutional Neural Network,” International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878, Vol.8, No.6, March 2020.
  16. R.S. Alkhawaldeh, “Arabic (Indian) digit handwritten recognition using recurrent transfer deep architecture,” Soft Computing Vol. 25, No.4, pp. 3131–3141, 2021.
  17. J. Christian, Z. Patrick, and H. Kai, "Machine Learning and Deep Learning," Electronic Markets, Vol.31, No. 3, pp. 685-695,2021.
  18. A. I. Khuder, Sh. H. Husain,” Hardware Realization of Artificial Neural Networks Using Analogue Devices,” Al-Rafidain Engineering, Vol.21, No.1, pp, 77-90, 2013.
  19. R. Dixit, R.Kushwah and S. Pashine,” Handwritten Digit Recognition using Machine and Deep Learning Algorithms,” International Journal of ComputerApplications, Vol.176, No.42, pp.27-33, July 2020.
  20. S.Haykin, “Neural networks and learning machines,” Third. Upper Saddle River, NJ: Pearson Education, 2009
  21. N. Chigozie, I. Winifred, G. Anthony, and M. Stephen,” Activation functions: Comparison of trends in practice and research for deep learning,”arXiv preprint arXiv:1811.03378 (2018).
  22. L.Alzubaidi, J. Zhang, A.J. Humaidi, et al.,” Review of deep learning: concepts, CNN architectures, challenges, applications,” Journal of Big Data, Vol. 8, pp.1-74, 2021.
  23. S. Indolia, A.K.Goswami, S.P. Mishra, P.Asopa,” Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” Procedia Computer Science, Vol.132, pp. 679-688, 2018.
  24. J. Wu, “Introduction to Convolutional Neural Networks,” National Key Lab for Novel Software Technology. Nanjing University. China, 2017.
  25. F.Siddique, S.Sakib, M.A.B.Siddique, "Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers," 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), 2019, pp. 541-546, doi: 10.1109/ICAEE48663.2019.8975496.
  26. R. Yamashita, M.Nishio, R.K.G. Do, et al.,” Convolutional neural networks: an overview and application in radiology,” Insights Imaging Vol. 9, pp. 611–629, 2018.
  27. S.Albawi, T.A.Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” 2017 International Conference on Engineering and Technology (ICET) (2017): pp.1-6,doi: 10.1109/ICEngTechnol.2017.8308186.
  28. H.M. Anwar, A.M. Mohon,” Recognition of handwritten digit using convolutional neural network (CNN),” Global Journal of Computer Science and Technology, Vol.19, No.2 pp.27-33, 2019.
  29.  S.Shibani, D.Tsipras, A. Ilyas and A. Madry, “How Does Batch Normalization Help Optimization?,” Advances in neural information processing systems Vol. 31, 2018.
  30. J.James, C. Lakshmi, U.P.Parthiban, “An Efficient Offline Hand Written Character Recognition using CNN and Xgboost,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Vol.8, No.6, April 2019.
  31. M. Schonlau, R.Y. Zou,” The random forest algorithm for statistical learning,” The Stata Journal, Vol. 20, No. 1, pp.3-29, 2020, doi:10.1177/1536867X20909688.
  32. Y. Sun, H. Zhang, T. Zhao, Z. Zou, B. Shen and L. Yang, "A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis," in IEEE Access, vol. 8, pp. 85421-85430, 2020, doi: 10.1109/ACCESS.2020.2992231.
  33. J. Ali, R. Khan, N. Ahmad and I. Maqsood “Random Forests and Decision Trees,” International Journal of Computer Science (IJCSI), Vol. 9, No. 5, pp. 272–278, 2012.
  34. Q. Ren, H. Cheng, and H. Han,” Research on Machine Learning Framework Based on Random Forest Algorithm,” In AIP conference proceedings, Vol. 1820, No. 1,2017.
  35. C.Yevhen, A. Serhiienko, I. Syrmamiikh and A.Kargin. “Handwritten Digits Recognition Using SVM, KNN, RF and Deep Learning Neural Networks,” CMIS, Vol.2864, pp. 496-509.
  36. D. Beohar and A. Rasool, "Handwritten Digit Recognition of MNIST dataset using Deep Learning state-of-the-art Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)," 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 2021, pp. 542-548, doi: 10.1109/ESCI50559.2021.9396870.
  37. S. Abdleazeem, E. El-Sherif, “Arabic handwritten digit recognition,” International Journal of Document Analysis and Recognition (IJDAR), Vol. 11, No. 3, pp. 127-141, 2008.
  38. A. M. Fadhil, M.Faris, A.Al-Saegh, M. Mohammad, "Real-Time Signature Recognition Using Neural Network, "Al-Rafidain Engineering Journal (AREJ), Vol.26, No.1, pp.159-165, 2021, doi: 10.33899/rengj.2021.129871.1088.
  39. S.Nitish, H.Geoffrey, K. Alex, S. Ilya, S.Ruslan, “Dropout: a simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, Vol.15, pp. 1929-1958, 2014.
  40. X. Qi, T. Wang, and J. Liu, "Comparison of Support Vector Machine and Softmax Classifiers in Computer Vision," 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2017, pp. 151-155, doi: 10.1109/ICMCCE.2017.49
  41. Scikit-learn. User guide.
  42. X. Baojun, H. Joshua, W. Graham, W.Qiang and Y. Yunming,” Classifying Very High-Dimensional Data with Random Forests Built from Small Subspaces,” International Journal of Data Warehousing and Mining, Vol. 8, No. 2, pp 44–63, 2012.