skip to main content
research-article

Arabic Handwritten Word Recognition Based on Stationary Wavelet Transform Technique using Machine Learning

Authors Info & Claims
Published:13 December 2021Publication History
Skip Abstract Section

Abstract

This paper is aimed at improving the performance of the word recognition system (WRS) of handwritten Arabic text by extracting features in the frequency domain using the Stationary Wavelet Transform (SWT) method using machine learning, which is a wavelet transform approach created to compensate for the absence of translation invariance in the Discrete Wavelets Transform (DWT) method. The proposed SWT-WRS of Arabic handwritten text consists of three main processes: word normalization, feature extraction based on SWT, and recognition. The proposed SWT-WRS based on the SWT method is evaluated on the IFN/ENIT database applying the Gaussian, linear, and polynomial support vector machine, the k-nearest neighbors, and ANN classifiers. ANN performance was assessed by applying the Bayesian Regularization (BR) and Levenberg-Marquardt (LM) training methods. Numerous wavelet transform (WT) families are applied, and the results prove that level 19 of the Daubechies family is the best WT family for the proposed SWT-WRS. The results also confirm the effectiveness of the proposed SWT-WRS in improving the performance of handwritten Arabic word recognition using machine learning. Therefore, the suggested SWT-WRS overcomes the lack of translation invariance in the DWT method by eliminating the up-and-down samplers from the proposed machine learning method.

REFERENCES

  1. [1] Nemmour H. and Chibani Y.. 2017. Artificial immune algorithm for handwritten Arabic word recognition. The International Arab Journal of Information Technology 14, 2 (2017), 186194.Google ScholarGoogle Scholar
  2. [2] El Qacimy B., Kerroum M. A., and Hammouch A.. 2015. Word-based Arabic handwritten recognition using SVM classifier with a reject option. 15th International Conference on Intelligent Systems Design and Applications (ISDA). 6468. DOI: DOI: 10.1109/ISDA.2015.7489190Google ScholarGoogle ScholarCross RefCross Ref
  3. [3] Parvez M. T. and Mahmoud S. A.. 2013. Offline Arabic handwritten text recognition: A survey. ACM Computing Surveys (CSUR) 45, 2 (2013), 23. http://doi.acm.org/10.1145/2431211.2431222 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. [4] Khorsheed M.. 2002. Off-line Arabic character recognition – a review. 2002. Pattern Anal Appl 5 (2002), 3145. https://doi.org/10.1007/s100440200004 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Nemmour H. and Chibani Y.. 2011. Handwritten Arabic word recognition based on ridgelet transform and support vector machines. 2011 International Conference on High Performance Computing & Simulation. 357361. DOI: DOI: 10.1109/HPCSim.2011.5999846Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Al-Saqqar F., AL-Shatnawi A., Al-Diabat M., and Aloun M.. 2019. Handwritten Arabic text recognition using principal component analysis and support vector machines. International Journal of Advanced Computer Science and Applications 10, 12 (2019), 16. DOI: http://dx.doi.org/10.14569/IJACSA.2019.0101227Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Nashwan F., Rashwan M. A., Al-Barhamtoshy H. M., Abdou S. M., and Moussa A. M.. 2018. A holistic technique for an Arabic OCR system. Journal of Imaging 4, 1 (2018), 111. https://doi.org/10.3390/jimaging4010006Google ScholarGoogle Scholar
  8. [8] World Arabic Language Day. 2017. UNESCO. Archived from the original on 27 October 2017. Accessed 21 January 2020.Google ScholarGoogle Scholar
  9. [9] Al-Shatnawi A., AlFawwaz B., Omar K., and Zeki A.. 2014. Skeleton extraction: Comparison of five methods on the Arabic IFN/ENIT database. 6th International Conference on Computer Science and Information Technology (CSIT). 5059. DOI: DOI: 10.1109/CSIT.2014.6805978Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Al-Shatnawi A.. 2014. A skew detection and correction technique for Arabic script text-line based on subwords bounding. 2014 IEEE International Conference in Computational Intelligence and Computing Research (ICCIC). 15. DOI: DOI: 10.1109/ICCIC.2014.7238501Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Al-Shatnawi A.. 2016. A novel baseline estimation method for Arabic handwritten text based on exploited components of Voronoi diagrams. International Arab Journal of Information Technology 13, 3 (2016), 9771003.Google ScholarGoogle Scholar
  12. [12] Al-Shatnawi A.. 2015. A preprocessing model for hand-written Arabic texts based on Voronoi diagrams. International Journal of Computer Science and Information Technology 7, 6 (2015), 118. https://doi.org/10.5121/ijcsit.2015.7601Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] El Qacimy B., Hammouch A., and Kerroum M. A.. 2015. A review of feature extraction techniques for handwritten Arabic text recognition. 2015 International Conference on Electrical and Information Technologies (ICEIT). 241245. DOI: DOI: 10.1109/EITech.2015.7162979Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] AlKhateeb J. H.. 2010. Word based off-line handwritten Arabic classification and recognition: Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches. Ph.D. dissertation, University of Bradford.Google ScholarGoogle Scholar
  15. [15] AlKhateeb J. H.. 2011. Word-based handwritten Arabic scripts recognition using dynamic Bayesian network. In Proceedings of the 5th International Conference on Information Technology.Google ScholarGoogle Scholar
  16. [16] El-Hajj R., Likforman-Sulem L., and Mokbel C.. 2005. Arabic handwriting recognition using baseline dependent features and hidden Markov modeling. In Eighth International Conference on Document Analysis and Recognition (ICDAR'05). 893897. DOI: DOI: 10.1109/ICDAR.2005.53 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Al-Shatnawi A. M., Safwan A. S., AL-Zawaideh F., and Omar K.. 2011. Offline Arabic text recognition–an overview. World of Computer Science and Information Technology Journal (WCSIT) 1, 5 (2011), 184192.Google ScholarGoogle Scholar
  18. [18] Hassan A. K. A. and Alawi M.. 2017. Proposed handwriting Arabic words classification based on discrete wavelet transform and support vector machine. Iraqi Journal of Science 58, 2C (2017), 11591168.Google ScholarGoogle Scholar
  19. [19] Kadhm M. S. and Abdul A.. 2015. Handwriting word recognition based on SVM classifier. International Journal of Advanced Computer Science & Applications 1 (2015), 6468.Google ScholarGoogle Scholar
  20. [20] Khalifa M. and BingRu Y.. 2011. A novel word based Arabic handwritten recognition system using SVM classifier. In International Conference on Electronic Commerce, Web Application, and Communication. Springer, 163171. https://doi.org/10.1007/978-3-642-20367-1_26Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Chaplot S., Patnaik L. M., and Jagannathan N. R.. 2006. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control 1, 1 (2006), 8692. https://doi.org/10.1016/j.bspc.2006.05.002Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Qayyum H., Majid M., Anwar S. M., and Khan B.. 2017. Facial expression recognition using stationary wavelet transform features. Mathematical Problems in Engineering. 19. https://doi.org/10.1155/2017/9854050Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Ali N. A.. 2015. Face recognition using stationary wavelet transform and neural network with support vector machine. Iraqi Journal of Science 56, 1B (2015), 520530.Google ScholarGoogle Scholar
  24. [24] Ghadhban H. Q., Othman M., Samsudin N. A., Ismail M. N. B., and Hammoodi M. R.. 2020. Survey of offline Arabic handwriting word recognition. In International Conference on Soft Computing and Data Mining. Springer, Cham, 358372. https://doi.org/10.1007/978-3-030-36056-6_34Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Dehghan M., Faez K., Ahmadi M., and Shridhar M.. 2001. Handwritten Farsi (Arabic) word recognition: A holistic approach using discrete HMM. Pattern Recognition 34, 5 (2001), 10571065. https://doi.org/10.1016/S0031-3203(00)00051-0Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Pechwitz M., Maergner V., and El Abed H.. 2006. Comparison of two different feature sets for offline recognition of handwritten Arabic words. Tenth International Workshop on Frontiers in Handwriting Recognition.Google ScholarGoogle Scholar
  27. [27] El Affar A., Ferdous K., El Fadili H., and Qjidaa H.. 2009. Krawtchouk moment feature extraction for neural Arabic handwritten words recognition. 2009 International Conference on Multimedia Computing and Systems. 443448. DOI: DOI: 10.1109/MMCS.2009.5256656Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] El-Bashir M.. 2010. Principal components analysis for Arabic sub-word recognition. 2010 International Conference on Intelligent Network and Computing (ICINC 2010). 432434.Google ScholarGoogle Scholar
  29. [29] AlKhateeb J. H., Ren J., Jiang J., Al-Muhtaseb H.. 2011. Offline handwritten Arabic cursive text recognition using hidden Markov models and re-ranking. Pattern Recognition Letters 32, 8 (2011), 10811088. https://doi.org/10.1016/j.patrec.2011.02.006 Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Sagheer M. W., He C. L., Nobile N., and Suen C. Y.. 2010. Holistic Urdu handwritten word recognition using support vector machine. 20th International Conference on Pattern Recognition. 19001903. DOI: DOI: 10.1109/ICPR.2010.468 Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [31] Alalshekmubarak A., Hussain A., and Wang Q. F.. 2012. Off-line handwritten Arabic word recognition using SVMs with normalized poly kernel. In International Conference on Neural Information Processing. Springer, 8591. https://doi.org/10.1007/978-3-642-34481-7_11 Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Nemouchi S., Meslati L. S., and Farah N.. 2012. Classifiers combination for Arabic words recognition: Application to handwritten Algerian city names. International Conference on Image and Signal Processing. Springer, 562570. https://doi.org/10.1007/978-3-642-31254-0_64 Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Khémiri A., Echi A. K., Belaïd A., Elloumi M.. 2015. Arabic handwritten words off-line recognition based on HMMs and DBNs. 13th International Conference on Document Analysis and Recognition (ICDAR). 5155. DOI: DOI: 10.1109/ICDAR.2015.7333724 Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Hassen H. and Al-Maadeed S.. 2017. Arabic handwriting recognition using sequential minimal optimization. 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). 7984. DOI: DOI: 10.1109/ASAR.2017.8067764Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Maalej R. and Kherallah M.. 2018. Convolutional neural network and BLSTM for offline Arabic handwriting recognition. In 2018 International Arab Conference on Information Technology (ACIT). 16. DOI: DOI: 10.1109/ACIT.2018.8672667Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Amrouch M., Rabi M., and Es-Saady Y.. 2018. Convolutional feature learning and CNN based HMM for Arabic handwriting recognition. In International Conference on Image and Signal Processing. 265274. https://doi.org/10.1007/978-3-319-94211-7_29Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Rabi M., Amrouch M., and Mahani Z.. 2018. Recognition of cursive Arabic handwritten text using embedded training based on hidden Markov models. International Journal of Pattern Recognition and Artificial Intelligence 32, 1 (2018), 1860007.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Tavoli R., Keyvanpour M., and Mozaffari S.. 2018. Statistical geometric components of straight lines (SGCSL) feature extraction method for offline Arabic/Persian handwritten words recognition. IET Image Processing 12, 9 (2018), 16061616.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Awni M., Khalil M. I., and Abbas H. M.. 2019. Deep-learning ensemble for offline Arabic handwritten words recognition. In 2019 14th International Conference on Computer Engineering and Systems (ICCES). IEEE, 4045. DOI: DOI: 10.1109/ICCES48960.2019.9068184Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Khémiri A., Echi A. K., and Elloumi M.. 2019. Bayesian versus convolutional networks for Arabic handwriting recognition. Arab J Sci Eng 44, 93019319. https://doi.org/10.1007/s13369-019-03939-yGoogle ScholarGoogle ScholarCross RefCross Ref
  41. [41] Hamida S., Cherradi B., and Ouajji H.. 2020. Handwritten Arabic words recognition system based on hog and Gabor filter descriptors. In 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 14. DOI: DOI: 10.1109/IRASET48871.2020.9092067Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Saidi A., Lakhdar A. M., and Beladgham M.. 2021. Recognition of offline handwritten Arabic words using a few structural features. Computers, Materials & Continua 66, 3 (2021), 28752889. doi: DOI: 10.32604/cmc.2021.013744Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Nason G. P. and Silverman B. W.. 1995. The stationary wavelet transform and some statistical applications. In Wavelets and Statistics. Springer, 281299. DOI: https://doi.org/10.1007/978-1-4612-2544-7_17Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Moen H.. 2007. Wavelet transforms and efficient implementation on the GPU. University of Oslo, Master's thesis.Google ScholarGoogle Scholar
  45. [45] Zhang Y., Wang S., Huo Y., Wu L., and Liu A.. 2010. Feature extraction of brain MRI by stationary wavelet transform and its applications. Journal of Biological Systems. 18, (spec01), 115132. https://doi.org/10.1142/S0218339010003652Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Zhang Y., Dong Z., Liu A., Wang S., Ji G., Zhang Z., and Yang J.. 2015. Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. Journal of Medical Imaging and Health Informatics 5, 7 (2015), 13951403. https://doi.org/10.1166/jmihi.2015.1542Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Jayech K., Mahjoub M. A., and Amara N. E. B.. 2016. Synchronous multi-stream hidden Markov model for offline Arabic handwriting recognition without explicit segmentation. Neurocomputing. 214, 958971. https://doi.org/10.1016/j.neucom.2016.07.020 Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Pesquet J. C., Krim H., and Carfantan H.. 1996. Time-invariant orthonormal wavelet representations. IEEE Transactions on Signal Processing 44, 8 (1996), 19641970. https://doi.org/10.1109/78.533717 Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Vapnik V.. 1998. Statistical Learning Theory, New York Wiley. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Amara M., Ghedira K., Zidi K., and Zidi S.. 2015. A comparative study of multi-class support vector machine methods for Arabic characters recognition. 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA). 16. DOI: DOI: 10.1109/AICCSA.2015.7507240Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Alamri H., He C. L., and Suen C. Y.. 2009. A new approach for segmentation and recognition of Arabic handwritten touching numeral pairs. International Conference on Computer Analysis of Images and Patterns. Springer, 165172. https://doi.org/10.1007/978-3-642-03767-2_20. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. [52] Alaei A., Pal U., and Nagabhushan P.. 2009. Using modified contour features and SVM based classifier for the recognition of Persian/Arabic handwritten numerals. 2009 Seventh International Conference on Advances in Pattern Recognition. IEEE, 391394. DOI: DOI: 10.1109/ICAPR.2009.14 Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] El Qacimy B., Kerroum M. A., and Hammouch A.. 2014. Handwritten digit recognition based on DCT features and SVM classifier. 2014 Second World Conference on Complex Systems (WCCS). IEEE, 1316. DOI: DOI: 10.1109/ICoCS.2014.7060935Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Mahmoud S. A. and Olatunji S. O.. 2009. Automatic recognition of off-line handwritten Arabic (Indian) numerals using support vector and extreme learning machines. International Journal of Imaging 2, (A09) (2009), 3453.Google ScholarGoogle Scholar
  55. [55] Kouamo S. and Tangha C.. 2012. Handwritten character recognition with artificial neural networks. In Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing. Omatu J. De Paz Santana S., González S., Molina J., Bernardos A., and Rodríguez J. (eds). Springer, Berlin, 151. https://doi.org/10.1007/978-3-642-28765-7_64Google ScholarGoogle Scholar
  56. [56] Zakaria M., AL-Shebany M., and Sarhan S.. 2014. Artificial neural network: A brief overview. In International Journal of Engineering Research and Applications 4, 2 (2014), 0712.Google ScholarGoogle Scholar
  57. [57] Levenberg K.. 1944. A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, (1944), 196–168.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Marquardt D. W.. 1963. An algorithm for least squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11, 2 (1963), 431–-441.Google ScholarGoogle ScholarCross RefCross Ref
  59. [59] Yaqub M., Eren B., and Eyüpoğlu V.. 2016. Assessment of neural network training algorithms for the prediction of polymeric inclusion membranes efficiency. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi 20, 3 (2016), 533542.Google ScholarGoogle Scholar
  60. [60] Watkins D. S.. 2005. The least squares problem. Fundam. Matrix Comput. 1989, 181259.Google ScholarGoogle Scholar
  61. [61] MacKay D. J. C.. 1992. A practical Bayesian framework for backpropagation networks. Neural Compute 4, 3 (1992), 448472. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. [62] Pechwitz M., Maddouri S. S., Märgner V., Ellouze N., and Amiri H.. 2002. IFN/ENIT-database of handwritten Arabic words. In Proc. of CIFED. CiteSeer, 127136.Google ScholarGoogle Scholar
  63. [63] Märgner V. and El Abed H.. 2009. ICDAR 2009 Arabic handwriting recognition competition. 2009 10th International Conference on Document Analysis and Recognition. IEEE, 13831387. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Arabic Handwritten Word Recognition Based on Stationary Wavelet Transform Technique using Machine Learning

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 21, Issue 3
      May 2022
      413 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3505182
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 December 2021
      • Revised: 1 June 2021
      • Accepted: 1 June 2021
      • Received: 1 December 2020
      Published in tallip Volume 21, Issue 3

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    View Full Text

    HTML Format

    View this article in HTML Format .

    View HTML Format
    About Cookies On This Site

    We use cookies to ensure that we give you the best experience on our website.

    Learn more

    Got it!