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Automatic recognition of handwritten Arabic characters: a comprehensive review

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Abstract

The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field.

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Abbreviations

Adam:

An algorithm and not an acronym

AHCR:

Arabic handwritten character recognition

AI:

Artificial intelligence

ANN:

Artificial neural network

ASCII:

American Standard Code for Information Interchange

CER:

Character error rate

CS:

Computer science

CTC:

Connectionist temporal classification

CMATER:

Center for Microprocessor Application for Training Education and Research

CNN:

Convolutional neural network

CV:

Computer vision

CUDA:

Compute Unified Device Architecture

DCT:

Discrete cosine transformation

DL:

Deep learning

DPI:

Dots per inch

DWT:

Discrete wavelet transform

EBPANN:

Error back-propagation artificial neural network

EKG:

Electrocardiogram

FC:

Fully connected

FCM:

Fuzzy C-means clustering

GPU:

Graphical processing unit

HMM:

Hidden Markov model

HCR:

Handwritten character recognition

HOG:

Histogram of oriented gradients

HDR:

Handwritten digital recognition

HP:

Hewlett–Packard

K-NN (KNN):

K-nearest neighbor

LR:

Learning rate

LSTM:

Long short-term memory network

LVQ:

Learning vector quantization

MD-BLSTM:

Multi-dimension bi-direction long short-term memory

ML:

Machine learning

MLP:

Multi-layer perceptron

MSA:

Modern standard Arabic

NLP:

Natural language processing

OCR:

Optical character recognition

OIAHCR:

Offline isolated Arabic handwritten character recognition system

PCA:

Principal component analysis

PDA:

Personal digital assistant

PPM:

Pages per minute

PR:

Pattern recognition

RBF:

Radial basis function

ReLU:

Rectified linear unit

RNN:

Recurrent neural network

SAE:

Stacked autoencoder

SGD:

Stochastic gradient descent

SSD:

Single shot multi-box detector

SVM:

Support vector machine

TPU:

Tensor processing unit

UN:

United States

UTF:

Unicode Transformation Format

WER:

Word error rate

XML:

eXtensible Markup Language

References

  1. Versteegh K (2014) Arabic language. Edinburgh University Press, Edinburgh

    Google Scholar 

  2. Suleiman Y (2003) The Arabic language and national identity. Edinburgh University Press, Edinburgh

    Google Scholar 

  3. Shaalan K, Al-Sheikh S, Oroumchian F (2012) Query expansion based-on similarity of terms for improving Arabic information retrieval. In: International conference on intelligent information processing. Springer, pp 167–176

  4. El-Desouky AI, Salem MM, El-Gwad AOA, Arafat H (1991) A handwritten Arabic character recognition technique for machine reader. In: Third international conference on software engineering for real time systems, 1991, pp 212–216

  5. Shirko O, Omar N, Arshad H, Albared M (2010) Machine translation of noun phrases from Arabic to English using transfer-based approach. J Comput Sci 6(3):350

    Google Scholar 

  6. Importance of the Arabic language. https://www.importanceoflanguages.com/importance-arabic-language/. Accessed 25 Nov 2019

  7. Hourani A (1983) Arabic thought in the liberal age 1798–1939. Cambridge University Press, Cambridge

    Google Scholar 

  8. Pattern recognition. https://www.britannica.com/technology/pattern-recognition-computer-science. Accessed on 01 Jan 2019

  9. Lensu A (2002) Computationally intelligent methods for qualitative data analysis. University of Jyväskylä, Jyväskylä

    Google Scholar 

  10. Vadwala MA, Suthar MK, Karmakar MY, Thakkar N (2017) Survey paper on different speech recognition algorithm: challenges and techniques. Int J Comput Appl 175:31–36

    Google Scholar 

  11. Lawgali A (2015) A survey on Arabic character recognition. Int J Signal Process Image Process Pattern Recognit 8:401–426

    Google Scholar 

  12. Govindan V, Shivaprasad A (1990) Character recognition—a review. Pattern Recognit 23(7):671–683

    Google Scholar 

  13. Biadsy F, Saabni R, El-Sana J (2011) Segmentation-free online Arabic handwriting recognition. Int J Pattern Recognit Artif Intell 25(07):1009–1033

    Google Scholar 

  14. Tappert CC, Suen CY, Wakahara T (1990) The state of the art in online handwriting recognition. IEEE Trans Pattern Anal Mach Intell 12(8):787–808

    Google Scholar 

  15. Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 22(1):63–84

    Google Scholar 

  16. Klatt DH (1987) Review of text-to-speech conversion for English. J Acoust Soc Am 82(3):737–793

    Google Scholar 

  17. Bijl D, Hyde-Thomson H (2001) Speech to text conversion. Google Patents, ed

  18. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

    MATH  Google Scholar 

  19. Manning CD, Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT Press, Cambridge

    MATH  Google Scholar 

  20. Shaalan K, Siddiqui S, Alkhatib M, Abdel Monem A (2018) Challenges in Arabic natural language processing. World Scientific, Singapore, pp 59–83

    Google Scholar 

  21. Nikolajeva M (2014) “Reading for learning,” cognitive approaches to children’s literature. John Benjamins Publishing Company, Amsterdam

    Google Scholar 

  22. Nasukawa T (1998) Parsing method and system for natural language processing. Google Patents, ed

  23. Busch JE, Lin AD, Graydon PJ, Caudill M (2006) Ontology-based parser for natural language processing. Google Patents, ed

  24. Mitchell DC (1994) Sentence parsing. In: Gernsbacher MA (ed) Handbook of psycholinguistics. Academic Press, New York, pp 375–409

    Google Scholar 

  25. Ueda H (1989) Word processor including spelling verifier and corrector. Google Patents, ed

  26. Zitouni I, Sorensen J, Luo X, Florian R (2005) The impact of morphological stemming on Arabic mention detection and coreference resolution. In: Proceedings of the ACL workshop on computational approaches to semitic languages. Association for Computational Linguistics, pp 63–70

  27. Lyons J (1977) Semantics. Cambridge University Press, Cambridge

    Google Scholar 

  28. Jackendoff R (1983) Semantics and cognition. MIT Press, Cambridge

    Google Scholar 

  29. Geeraerts D (2010) Theories of lexical semantics. Oxford University Press, Oxford

    Google Scholar 

  30. Zouaghi A, Zrigui M, Antoniadis G, Merhbene L (2012) Contribution to semantic analysis of Arabic language. Adv Artif Intell 2012

  31. Boudad N, Faizi R, Thami ROH, Chiheb R (2018) Sentiment analysis in Arabic: a review of the literature. Ain Shams Eng J 9(4):2479–2490

    Google Scholar 

  32. Tartir S, Abdul-Nabi I (2017) Semantic sentiment analysis in Arabic social media. J King Saud Univ - Comput Inf Sci 29(2):229–233

    Google Scholar 

  33. Alami N, El Adlouni Y, En-nahnahi N, Meknassi M (2018) Using statistical and semantic analysis for Arabic text summarization. In: International conference on information technology and communication systems. Springer, Cham, pp 35–50

  34. Salam M, Hassan AA (2019) Offline isolated Arabic handwriting character recognition system based on SVM. Int Arab J Inf Technol 16(3):467–472

    Google Scholar 

  35. Ko D, Lee C, Han D, Ohk H, Kang K, Han S (2018) Approach for machine-printed Arabic character recognition: the-state-of-the-art deep-learning method. Electron Imaging 2018(2):1–8

    Google Scholar 

  36. 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). IEEE, pp 1–4

  37. Balci B, Saadati D, Shiferaw D (2017) Handwritten text recognition using deep learning. CS231n: convolutional neural networks for visual recognition, Stanford University, Course Project Report, Spring

  38. Younis KS (2017) Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian J Comput Inf Technol 3(3):186–200

    Google Scholar 

  39. Ahmed SB, Naz S, Razzak MI, Yousaf R (2017) Deep learning based isolated Arabic scene character recognition. In: 2017 1st international workshop on Arabic script analysis and recognition (ASAR). IEEE, pp 46–51

  40. Loey M, El-Sawy A, EL-Bakry H (2017) Deep learning autoencoder approach for handwritten Arabic digits recognition. arXiv preprint arXiv:1706.06720

  41. El-Sawy A, Hazem E-B, Loey M (2016) CNN for handwritten Arabic digits recognition based on LeNet-5. In: International conference on advanced intelligent systems and informatics. Springer, pp 566–575

  42. Abdalkafor A, Alhamouz S (2016) Arabic offline handwritten isolated character recognition system using neural network. Int J Bus ICT 2:41–50

    Google Scholar 

  43. Abdalkafor AS, Sadeq A (2016) Arabic offline handwritten isolated character recognition system using neural network. Int J Bus ICT 2(3):41–50

    Google Scholar 

  44. Sahloul A, Suen C (2014) OFF-line system for the recognition of handwritten Arabic character. In: Fourth international conference on computer science & information technology, pp 227–244

  45. Nawaz SN, Sarfraz M, Zidouri A, Al-Khatib WG (2004) An approach to offline Arabic character recognition using neural networks, vol 3, pp 1328–1331

  46. Sarfraz M, Nawaz SN, Al-Khuraidly A (2003) Offline Arabic text recognition system, pp 30–35

  47. Al-Thubaity AO (2015) A 700 M+ Arabic corpus: KACST Arabic corpus design and construction. Langu Resources Eval J Artic 49(3):721–751

    Google Scholar 

  48. Das N, Mollah AF, Saha S, Haque SS (2010) Handwritten Arabic numeral recognition using a multi layer perceptron. arXiv preprint arXiv:1003.1891

  49. Jamal AT, Nobile N, Suen CY (2014) End-shape recognition for Arabic handwritten text segmentation. In: IAPR workshop on artificial neural networks in pattern recognition. Springer, pp 228–239

  50. Sahlol AT, Suen CY, Elbasyoni MR, Sallam AA (2014) Investigating of preprocessing techniques and novel features in recognition of handwritten Arabic characters. In: IAPR workshop on artificial neural networks in pattern recognition. Springer, pp 264–276

  51. Sahlol AT, Suen CY, Elbasyouni MR, Sallam AA (2014) A proposed OCR algorithm for the recognition of handwritten Arabic characters. J Pattern Recognit Intell Syst 2:8–22

    Google Scholar 

  52. Al-Muhtaseb HA, Mahmoud SA, Qahwaji RS (2008) Recognition of off-line printed Arabic text using Hidden Markov Models. Sig Process 88(12):2902–2912

    MATH  Google Scholar 

  53. Hamid A, Haraty R (2001) A neuro-heuristic approach for segmenting handwritten Arabic text. In: Proceedings ACS/IEEE international conference on computer systems and applications. IEEE, pp 110–113

  54. Pal U, Chaudhuri B (2004) Indian script character recognition: a survey. Pattern Recognit 37(9):1887–1899

    Google Scholar 

  55. Ward JR, Kuklinski T (1988) A model for variability effects in handprinting with implications for the design of handwriting character recognition systems. IEEE Trans Syst Man Cybern 18(3):438–451

    Google Scholar 

  56. Kholmatov A, Yanikoglu B (2005) Identity authentication using improved online signature verification method. Pattern Recognit Lett 26(15):2400–2408

    Google Scholar 

  57. https://www.pinterest.com/pin/349943833544343951/. Accessed on 25 May 2019

  58. PPM (pages per minute). https://whatis.techtarget.com/definition/PPM-pages-per-minute. Accessed on 25 May 2019

  59. Mackay DG, Zeller C, Cordery RA, Brunk HL (2003) Method for determining a printer’s signature and the number of dots per inch printed in a document to provide proof that the printer printed a particular document. Google Patents, ed

  60. Farooq F, Govindaraju V, Perrone M (2005) Pre-processing methods for handwritten Arabic documents. In: Eighth international conference on document analysis and recognition (ICDAR’05). IEEE, pp 267–271

  61. Tsang V, Jacob D, Shein F (2014) System and method for enhancing comprehension and readability of text. Google Patents, ed

  62. Trier OD, Taxt T (1995) Evaluation of binarization methods for document images. IEEE Trans Pattern Anal Mach Intell 17(3):312–315

    Google Scholar 

  63. Long J, Jin L (2004) An image binarization method based on global mean and local standard deviation. Comput Eng 2

  64. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146–166

    Google Scholar 

  65. Baligar VP, Patnaik LM, Nagabhushana G (2006) Low complexity, and high fidelity image compression using fixed threshold method. Inf Sci 176(6):664–675

    MathSciNet  Google Scholar 

  66. Xu X, Xu S, Jin L, Song E (2011) Characteristic analysis of Otsu threshold and its applications. Pattern Recognit Lett 32(7):956–961

    Google Scholar 

  67. Greensted A (2019) Otsu thresholding. http://www.labbookpages.co.uk/software/imgProc/otsuThreshold.html. Accessed on 25 May 2019

  68. Yousefi J (2015) Image binarization using Otsu thresholding algorithm. University of Guelph, Guelph

    Google Scholar 

  69. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Google Scholar 

  70. Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 2. IEEE, pp 60–65

  71. Roushdy M (2006) Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter. GVIP J 6(4):17–23

    Google Scholar 

  72. Verma R, Ali J (2013) A comparative study of various types of image noise and efficient noise removal techniques. Int J Adv Res Comput Sci Softw Eng 3(10):617–622

    Google Scholar 

  73. Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imaging 18(11):1049–1075

    Google Scholar 

  74. Chen J, Benesty J, Huang Y, Doclo S (2006) New insights into the noise reduction Wiener filter. IEEE Trans Audio Speech Lang Process 14(4):1218–1234

    Google Scholar 

  75. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Google Scholar 

  76. Sun C, Si D (1997) Skew and slant correction for document images using gradient direction. In: Proceedings of the fourth international conference on document analysis and recognition, vol 1. IEEE, pp 142–146

  77. Nagabhushan P, Alaei A (2010) Tracing and straightening the baseline in handwritten Persian/Arabic text-line: a new approach based on painting-technique. Int J Comput Sci Eng 2(4):907–916

    Google Scholar 

  78. Atallah A-S, Omar K (2008) Methods of Arabic language baseline detection—the state of art. IJCSNS 8(10):137

    Google Scholar 

  79. Sansom-Wai CY, Williams IH, Tretter DR (2001) Image processing system with image cropping and skew correction. Google Patents, ed

  80. Kurniawan F, Khan AR, Mohamad D (2009) Contour vs non-contour based word segmentation from handwritten text lines. An experimental analysis. Int J Digital Content Technol Appl 3(2):127–131

    Google Scholar 

  81. Mahajan N, Jaidka K (2015) Various skew detection and correction techniques: a survey. Int J Adv Res Comput Sci Softw Eng 5:4

    Google Scholar 

  82. Pei S-C, Lin C-N (1995) Image normalization for pattern recognition. Image Vis Comput 13(10):711–723

    Google Scholar 

  83. Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recognit 38(12):2270–2285

    Google Scholar 

  84. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167

  85. Abu-Ain W, Abdullah SNHS, Bataineh B, Abu-Ain T, Omar K (2013) Skeletonization algorithm for binary images. Procedia Technol 11:704–709

    Google Scholar 

  86. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60

    Google Scholar 

  87. dos Santos Tanaka FHK, Aranha C (2019) Data augmentation using GANs. Proc Mach Learn Res XXX:1–16

    Google Scholar 

  88. Shao S, Wang P, Yan C (2019) Generative adversarial networks for data augmentation in machine fault diagnosis. Comput Ind 106:85–93

    Google Scholar 

  89. Liu W et al. (2016) Ssd: single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37

  90. Chung J (2018). Handwriting OCR: line segmentation with Gluon. https://medium.com/apache-mxnet/handwriting-ocr-line-segmentation-with-gluon-7af419f3a3d8. Accessed on 11 May 2019

  91. Papavassiliou V, Stafylakis T, Katsouros V, Carayannis G (2010) Handwritten document image segmentation into text lines and words. Pattern Recognit 43(1):369–377

    MATH  Google Scholar 

  92. Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Assoc Comput Linguist 5:135–146

    Google Scholar 

  93. How many words does the Arabic language have? https://www.quora.com/How-many-words-does-the-Arabic-language-have. Accessed on 30 Aug 2019

  94. Lu Y, Shridhar M (1996) Character segmentation in handwritten words—an overview. Pattern Recognit 29(1):77–96

    Google Scholar 

  95. Tesseract OCR. https://github.com/tesseract-ocr/tesseract. Accessed on 30 Aug 2019

  96. Hong Z-Q (1991) Algebraic feature extraction of image for recognition. Pattern Recognit 24(3):211–219

    MathSciNet  Google Scholar 

  97. Trier ØD, Jain AK, Taxt T (1996) Feature extraction methods for character recognition-a survey. Pattern Recognit 29(4):641–662

    Google Scholar 

  98. Wickerhauser MV (1996) Adapted wavelet analysis: from theory to software. AK Peters/CRC Press, Boca Raton

    MATH  Google Scholar 

  99. Illingworth J, Kittler J (1987) The adaptive hough transform. IEEE Trans Pattern Anal Mach Intell PAMI-9(5):690–698

    Google Scholar 

  100. Xin Y (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Google Scholar 

  101. Kohonen T, Hynninen J, Kangas J, Laaksonen J, Torkkola K (1996) LVQ PAK: the learning vector quantization program package. Technical report, Laboratory of Computer and Information Science

  102. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  103. Abe S (2005) Support vector machines for pattern classification. Springer, Berlin

    MATH  Google Scholar 

  104. Fine S, Singer Y, Tishby N (1998) The hierarchical hidden Markov model: analysis and applications. Mach Learn 32(1):41–62

    MATH  Google Scholar 

  105. Keller JM, Gray MR, Givens JA (1985) A fuzzy K-nearest neighbor algorithm. IEEE Trans Syst Man Cybern SMC-15(4):580–585

    Google Scholar 

  106. Dudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern SMC-6(4):325–327

    Google Scholar 

  107. Korns MF, May T (2019) Strong typing, swarm enhancement, and deep learning feature selection in the pursuit of symbolic regression-classification. In: Genetic programming theory and practice XVI. Springer, pp 59–84

  108. Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146

  109. Deng D, Liu H, Li X, Cai D (2018) Pixellink: detecting scene text via instance segmentation. In: Thirty-second AAAI conference on artificial intelligence

  110. Wang Y, Xu W (2018) Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decis Support Syst 105:87–95

    Google Scholar 

  111. Chatterjee A, Gupta U, Chinnakotla MK, Srikanth R, Galley M, Agrawal P (2019) Understanding emotions in text using deep learning and big data. Comput Hum Behav 93:309–317

    Google Scholar 

  112. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ (2017) Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging 30(4):449–459

    Google Scholar 

  113. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Google Scholar 

  114. Hidaka A, Kurita T (2017) Consecutive dimensionality reduction by canonical correlation analysis for visualization of convolutional neural networks. Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications, Vol 2017. The ISCIE symposium on stochastic systems theory and Its applications, pp 160–167

  115. Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp 37–49

  116. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  117. Oppermann A (2018) Deep autoencoders for collaborative filtering. https://towardsdatascience.com/deep-autoencoders-for-collaborative-filtering-6cf8d25bbf1d. Accessed 18 Sept 2019

  118. Deng N, Tian Y, Zhang C (2012) Support vector machines: optimization based theory, algorithms, and extensions. Chapman and Hall/CRC Press, Boca Raton

    MATH  Google Scholar 

  119. Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  120. Shatnawi M (2015) Off-line handwritten Arabic character recognition: a survey. In: Proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV), p 52. The Steering Committee of the World Congress in Computer Science, Computer

  121. Dale R (2000) Guides to quality in visual resource imaging-imaging systems: the range of factors affecting image quality

  122. Russ JC (2016) The image processing handbook. CRC Press, Boca Raton

    MATH  Google Scholar 

  123. Taha HY (2013) Reading and spelling in Arabic: linguistic and orthographic complexity. Theory Pract Lang Stud 3(5):721

    Google Scholar 

  124. Daher J (1998) Gender in linguistic variation: the variable (q) in Damascus Arabic. Amst Stud Theory Hist Linguist Sci Ser 4:183–208

    Google Scholar 

  125. Russo F, Ramponi G (1996) A fuzzy filter for images corrupted by impulse noise. IEEE Signal Process Lett 3(6):168–170

    Google Scholar 

  126. Slimane F, Kanoun S, Hennebert J, Alimi AM, Ingold R (2013) A study on font-family and font-size recognition applied to Arabic word images at ultra-low resolution. Pattern Recognit Lett 34(2):209–218

    Google Scholar 

  127. Vogel CR, Oman ME (1998) Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans Image Process 7(6):813–824

    MathSciNet  MATH  Google Scholar 

  128. Awel MA, Abidi AI (2019) Review on optical character recognition. Int Res J Eng Technol 6:3666–3669

    Google Scholar 

  129. Torki M, Husseiny ME, Elsallamy A, Fayyaz M, Yaser S (2014) Window-based descriptors for Arabic handwritten alphabet recognition: a comparative study on a novel dataset

  130. Loey M (2019) Arabic handwritten characters dataset. https://www.kaggle.com/mloey1/ahcd1. Accessed on 31 Aug 2019

  131. El-Sawy A, Loey M, Hazem E (2017) Arabic handwritten characters recognition using convolutional neural network. WSEAS Trans Comput Res 5:11–19

    Google Scholar 

  132. Alamri H, Sadri J, Suen CY, Nobile N (2008) A novel comprehensive database for Arabic off-line handwriting recognition. In: Proceedings of 11th international conference on frontiers in handwriting recognition, ICFHR, vol 8, pp 664–669

  133. Eikvil L (1993) OCR-optical character recognition

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Authors and Affiliations

  1. Computer Engineering and Systems Department, Faculty of Engineering, Mansoura University, ‎Mansoura, Egypt

    Hossam Magdy Balaha & Hesham Arafat Ali

  2. Computer Engineering and Systems Department, Faculty of Engineering, Mansoura University, ‎Mansoura, Egypt

    Mahmoud Badawy

  3. Department of Computer Science and Informatics, Taibah University Al Medina Al Munawara, ‎Medina, Saudi Arabia

    Mahmoud Badawy

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  1. Hossam Magdy Balaha
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Balaha, H., Ali, H. & Badawy, M. Automatic recognition of handwritten Arabic characters: a comprehensive review. Neural Comput & Applic 33, 3011–3034 (2021). https://doi.org/10.1007/s00521-020-05137-6

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