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An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition

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Abstract

Many variations of local binary pattern (LBP) were proposed to enhance its performance, including uniform local binary pattern (ULBP), center-symmetric local binary patterns (CS-LBP), center symmetric local ternary patterns (CS-LTP), center symmetric local multilevel pattern (CS-LMP), etc. In this paper, the accuracies of LBP technique and its variations are enhanced using four different sizes of a sliding window approach. This approach is used for investigating whether the features extracted by LBP are significant enough or its versions are needed as well. Five LBP-based techniques have been used including LBP, CS-LBP, CS-LTP, CS-LMP, and U2LBP. They have been applied to an Arabic digit image dataset called MAHDBase. Support vector machine (SVM) and random forests are utilized as classifiers. The experimental results show that the obtained accuracies have been improved by 19.56%, 21.43%, 5.63%, 6.51% and 5.62% for CS-LBP, CS-LMP, U2LBP, CS-LTP, and LBP, respectively, when the sliding window approach has been applied and SVM with linear kernel has been used as a classifier. Moreover, the results show that there is no need to use LBP variations to enhance the accuracy if the sliding window is applied because the highest accuracy has been acquired using LBP. At the end, the accuracy of proposed systems has been compared against other state-of-the-art LBP-based techniques showing the significance of the proposed systems.

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References

  1. Alghazo JM, Latif G, Alzubaidi L, Elhassan A (2019) Multi-language handwritten digits recognition based on novel structural features. J imaging Sci Technol 63:20502-1–20502-10. https://doi.org/10.2352/J.ImagingSci.Technol.2019.63.2.020502

    Article  Google Scholar 

  2. AlKhateeb JH, Alseid M (2014) DBN - based learning for Arabic handwritten digit recognition using DCT features. In: 2014 6th international conference on computer science and information technology (CSIT). pp 222–226

  3. Alkhawaldeh RS (2020) Arabic (Indian) digit handwritten recognition using recurrent transfer deep architecture. Soft Comput 25:3131–3141. https://doi.org/10.1007/s00500-020-05368-8

    Article  Google Scholar 

  4. Almodfer R, Xiong S, Mudhsh M, Duan P (2017) Very deep neural networks for Hindi/Arabic offline handwritten digit recognition. In: Liu D, Xie S, Li Y, Zhao D, El-Alfy E-SM (eds) Neural information processing. Springer International Publishing, Cham, pp 450–459

    Chapter  Google Scholar 

  5. Al-Wajih E, Ahmed M (2020) A new application for gabor filters in face-based gender classification. Int Arab J Inf Technol 17:178–187. https://doi.org/10.34028/iajit/17/2/5

    Article  Google Scholar 

  6. Al-wajih E, Ghouti L (2019) Gender recognition using four statistical feature techniques: a comparative study of performance. Evol Intell 12:633–646. https://doi.org/10.1007/s12065-019-00264-z

    Article  Google Scholar 

  7. Al-wajih E, Ghazali R, Hassim YMM (2020) Residual neural network Vs local binary convolutional neural networks for bilingual handwritten digit recognition. In: Ghazali R, Nawi NM, Deris MM, Abawajy JH (eds) Recent advances on soft computing and data mining. Springer International Publishing, Cham, pp 25–34

    Chapter  Google Scholar 

  8. Arbain NA, Azmi MS, Muda AK, Muda NA, Radzid AR (2018) Offline handwritten digit recognition using triangle geometry properties. Int J Comput Inf Syst Ind Manag Appl 10:87–97

    Google Scholar 

  9. Ashiquzzaman A, Tushar AK (2017) Handwritten Arabic numeral recognition using deep learning neural networks. In: 2017 IEEE international conference on imaging, vision pattern recognition (icIVPR). pp 1–4

  10. Badeka E, Papadopoulou CI, Papakostas GA (2020) Evaluation of LBP variants in retinal blood vessels segmentation using machine learning. In: 2020 international conference on intelligent systems and computer vision (ISCV). pp 1–7

  11. Balili CC, Sobrepena MCC, Naval PC (2015) Classification of heart sounds using discrete and continuous wavelet transform and random forests. In: 2015 3rd IAPR Asian conference on pattern recognition (ACPR). pp 655–659

  12. Biglari M, Mirzaei F, Neycharan JG (2014) Persian/Arabic handwritten digit recognition using local binary pattern. Int J Digit Inf Wirel Commun IJDIWC 4:486–492

    Google Scholar 

  13. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory (pp. 144–152). Association for Computing Machinery, New York

  14. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  15. Brodatz P (1966) Textures: a photographic album for artists and designers, 1st edn. Dover Pubns, New York

    Google Scholar 

  16. Can YS, Kabadayı ME (2020) Automatic CNN-based Arabic numeral spotting and handwritten digit recognition by using deep transfer learning in ottoman population registers. Appl Sci 10:5430. https://doi.org/10.3390/app10165430

    Article  Google Scholar 

  17. Chan JC-W, Paelinckx D (2008) Evaluation of random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112:2999–3011. https://doi.org/10.1016/j.rse.2008.02.011

    Article  Google Scholar 

  18. Chen Y, Xiong J, Xu W, Zuo J (2019) A novel online incremental and decremental learning algorithm based on variable support vector machine. Clust Comput 22:7435–7445. https://doi.org/10.1007/s10586-018-1772-4

    Article  Google Scholar 

  19. Chen Y, Xu W, Zuo J, Yang K (2019) The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier. Clust Comput 22:7665–7675. https://doi.org/10.1007/s10586-018-2368-8

    Article  Google Scholar 

  20. CMATERdb 3.3.1: Handwritten Arabic numeral database. https://code.google.com/archive/p/cmaterdb/downloads. Accessed 11 Oct 2020

  21. El Khadiri I, Chahi A, El Merabet Y, Ruichek Y, Touahni R (2018) Local directional ternary pattern: a new texture descriptor for texture classification. Comput Vis Image Underst 169:14–27. https://doi.org/10.1016/j.cviu.2018.01.004

    Article  Google Scholar 

  22. El Khadiri I, Kas M, El Merabet Y, Ruichek Y, Touahni R (2018) Repulsive-and-attractive local binary gradient contours: new and efficient feature descriptors for texture classification. Inf Sci 467:634–653. https://doi.org/10.1016/j.ins.2018.02.009

    Article  Google Scholar 

  23. El Merabet Y, Ruichek Y (2018) Local concave-and-convex micro-structure patterns for texture classification. Pattern Recogn 76:303–322. https://doi.org/10.1016/j.patcog.2017.11.005

    Article  Google Scholar 

  24. El-Sawy A, El-Bakry H, Loey M (2017) CNN for handwritten Arabic digits recognition based on LeNet-5. In: Hassanien AE, Shaalan K, Gaber T, Azar AT, Tolba MF (eds) Proceedings of the international conference on advanced intelligent systems and informatics 2016. Springer International Publishing, Cham, pp 566–575

    Chapter  Google Scholar 

  25. El-Sherif EA, Abdelazeem S (2007) A two-stage system for Arabic handwritten digit recognition tested on a new large database. In: Artificial intelligence and pattern recognition, pp 237–242

    Google Scholar 

  26. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  27. Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15:3133–3181

    MathSciNet  MATH  Google Scholar 

  28. Ghaleb MH, George LE, Mohammed FG (2013) Numeral handwritten Hindi/Arabic numeric recognition method. Int J Sci Eng Res 4

  29. Ghofrani A, Toroghi RM (2019) Capsule-based Persian/Arabic robust handwritten digit recognition using EM routing. In: 2019 4th international conference on pattern recognition and image analysis (IPRIA). pp 168–172

  30. Gouveia C, Tomé A, Barros F, Soares SC, Vieira J, Pinho P (2020) Study on the usage feasibility of continuous-wave radar for emotion recognition. Biomed Signal Process Control 58:101835. https://doi.org/10.1016/j.bspc.2019.101835

    Article  Google Scholar 

  31. Gupta R, Patil H, Mittal A (2010) Robust order-based methods for feature description. In: 2010 IEEE computer society conference on computer vision and pattern recognition. pp 334–341

  32. Hasan AM, Jalab HA, Ibrahim RW, Meziane F, Al-Shamasneh AR, Obaiys SJ (2020) MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy 22:1033. https://doi.org/10.3390/e22091033

    Article  MathSciNet  Google Scholar 

  33. Hassan AKA (2018) Arabic (Indian) handwritten digits recognition using multi feature and KNN classifier. J Univ Babylon Pure Appl Sci 26:10–17. https://doi.org/10.29196/jub.v26i4.679

    Article  Google Scholar 

  34. Hassan T, Khan HA (2015) Handwritten Bangla numeral recognition using local binary pattern. In: 2015 international conference on electrical engineering and information communication technology (ICEEICT). pp 1–4

  35. Heikkilä M, Pietikäinen M, Schmid C (2009) Description of interest regions with local binary patterns. Pattern Recogn 42:425–436. https://doi.org/10.1016/j.patcog.2008.08.014

    Article  MATH  Google Scholar 

  36. Hirwani A, Verma N, Gonnade S (2014) Efficient handwritten alphabet recognition using LBP based feature extraction and nearest neighbor classifier. Int J Adv Res Comput Sci Softw Eng 4:549–553

    Google Scholar 

  37. Hsu C-W, Chang C-C, Lin C-J et al (2003) A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, Taipei, Taiwan

    Google Scholar 

  38. Ilmi N, Budi WTA, Nur RK (2016) Handwriting digit recognition using local binary pattern variance and K-nearest neighbor classification. In: 2016 4th international conference on information and communication technology (ICoICT). pp 1–5

  39. Jabid T, Kabir MH, Chae O (2010) Local directional pattern (LDP) for face recognition. In: 2010 digest of technical papers international conference on consumer electronics (ICCE). pp 329–330

  40. Jaha ES (2019) Efficient Gabor-based recognition for handwritten Arabic-Indic digits. Int J Adv Comput Sci Appl IJACSA 10. https://doi.org/10.14569/IJACSA.2019.0100114

  41. Jeyasudha A, Priya K (2016) Object recognition based on LBP and discrete wavelet transform. Int J Adv Signal Image Sci 2:24–30

    Article  Google Scholar 

  42. Jun Z, Jizhao H, Zhenglan T, Feng W (2017) Face detection based on LBP. In: 2017 13th IEEE international conference on electronic measurement instruments (ICEMI). pp 421–425

  43. Kaya Y, Ertuğrul ÖF, Tekin R (2015) Two novel local binary pattern descriptors for texture analysis. Appl Soft Comput 34:728–735. https://doi.org/10.1016/j.asoc.2015.06.009

    Article  Google Scholar 

  44. Kumar KK, Pavani M (2017) LBP based biometrie identification using the periocular region. In: 2017 8th IEEE annual information technology, electronics and Mobile communication conference (IEMCON). pp 204–209

  45. Lawgali A (2015) Handwritten digit recognition based on DWT and DCT. Int J Database Theory Appl 8:215–222. https://doi.org/10.14257/ijdta.2015.8.5.18

    Article  Google Scholar 

  46. Loey M, El-Sawy A, El-Bakry H (2017) Deep learning autoencoder approach for handwritten Arabic digits recognition. ArXiv170606720 Cs 533:566–575. https://doi.org/10.1007/978-3-319-48308-5_54

  47. Montazer GA, Soltanshahi MA, Giveki D (2017) Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method. Opt Mem Neural Netw 26:117–128. https://doi.org/10.3103/S1060992X17020060

    Article  Google Scholar 

  48. Mouli C, Ramalingam SP (2016) Dimensionality reduced local directional pattern (DR-LDP) for face recognition. Expert Syst Appl 63:66–73. https://doi.org/10.1016/j.eswa.2016.06.031

    Article  Google Scholar 

  49. Myers JL, Well AD, Lorch RF Jr (2010) Research design and statistical analysis, 3rd edn. Routledge, New York

    Google Scholar 

  50. Nanni L, Lumini A, Brahnam S (2012) Survey on LBP based texture descriptors for image classification. Expert Syst Appl 39:3634–3641. https://doi.org/10.1016/j.eswa.2011.09.054

    Article  Google Scholar 

  51. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  52. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987. https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  53. Oshiro TM, Perez PS, Baranauskas JA (2012) How many trees in a random Forest? In: Perner P (ed) Machine learning and data Mining in Pattern Recognition. Springer, Berlin, Heidelberg, pp 154–168

    Chapter  Google Scholar 

  54. Radwan E (2013) Hybrid of rough neural networks for Arabic/Farsi handwriting recognition. Int J Adv Res Artif Intell IJARAI 2. https://doi.org/10.14569/IJARAI.2013.020207

  55. Rakshit RD, Nath SC, Kisku DR (2017) An improved local pattern descriptor for biometrics face encoding: a LC–LBP approach toward face identification. J Chin Inst Eng 40:82–92. https://doi.org/10.1080/02533839.2016.1259020

    Article  Google Scholar 

  56. Rivera AR, Castillo JR, Chae OO (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22:1740–1752. https://doi.org/10.1109/TIP.2012.2235848

    Article  MathSciNet  MATH  Google Scholar 

  57. Shang J, Chen C, Liang H, Tang H (2016) Object recognition using rotation invariant local binary pattern of significant bit planes. IET Image Process 10:662–670. https://doi.org/10.1049/iet-ipr.2016.0058

    Article  Google Scholar 

  58. Sharif SMA, Mujtaba G, Nadim Uddin SM (2019) EdgeNet: A novel approach for Arabic numeral classification. ArXiv E-Prints arXiv 1908:02254

    Google Scholar 

  59. Shilbayeh NF, Aqel MM, Alkhateeb R (2013) Recognition offline handwritten Hindi digits using multilayer Perceptron neural networks. In: 2nd International Conference on Information Technology and Computer Networks, pp 94–103

    Google Scholar 

  60. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19:1635–1650. https://doi.org/10.1109/TIP.2010.2042645

    Article  MathSciNet  MATH  Google Scholar 

  61. Vapnik VN, Chervonenkis AY (2015) On the uniform convergence of relative frequencies of events to their probabilities. In: Vovk V, Papadopoulos H, Gammerman A (eds) Measures of complexity: Festschrift for Alexey Chervonenkis. Springer International Publishing, Cham, pp 11–30

    Chapter  Google Scholar 

  62. Weidner L, Walton G, Kromer R (2019) Classification methods for point clouds in rock slope monitoring: a novel machine learning approach and comparative analysis. Eng Geol 263:105326. https://doi.org/10.1016/j.enggeo.2019.105326

    Article  Google Scholar 

  63. Yuan F, Shi J, Xia X, Fang Y, Fang Z, Mei T (2016) High-order local ternary patterns with locality preserving projection for smoke detection and image classification. Inf Sci 372:225–240. https://doi.org/10.1016/j.ins.2016.08.040

    Article  Google Scholar 

  64. Zeebaree DQ, Haron H, Abdulazeez AM, Zebari DA (2019) Trainable model based on new uniform LBP feature to identify the risk of the breast Cancer. In: 2019 international conference on advanced science and engineering (ICOASE). pp 106–111

  65. Zeng H, Wang X, Gu Y (2016) Center symmetric local multilevel pattern based descriptor and its application in image matching. Int J Optoelectron 2016:1–9. https://doi.org/10.1155/2016/1584514

    Article  Google Scholar 

  66. Zhao Y, Wang R-G, Wang W-M, Gao W (2016) Local quantization code histogram for texture classification. Neurocomputing 207:354–364. https://doi.org/10.1016/j.neucom.2016.05.016

    Article  Google Scholar 

  67. Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384. https://doi.org/10.1016/j.neucom.2013.03.020

    Article  Google Scholar 

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This work was supported by the Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Higher Education (MOHE) Malaysia [grant number 1641].

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  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Parit Raja, Johor, Malaysia

    Ebrahim Al-wajih & Rozaida Ghazali

  2. Society Development & Continuing Education Center, Hodeidah University, Alduraihimi, 3114, Hodeidah, Yemen

    Ebrahim Al-wajih

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  1. Ebrahim Al-wajih
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Al-wajih, E., Ghazali, R. An enhanced LBP-based technique with various size of sliding window approach for handwritten Arabic digit recognition. Multimed Tools Appl 80, 24399–24418 (2021). https://doi.org/10.1007/s11042-021-10762-x

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