nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Arabic Handwriting Word Recognition Based on Convolutional Recurrent Neural Network

verfasst von : Manal Boualam, Youssef Elfakir, Ghizlane Khaissidi, Mostafa Mrabti

Erschienen in: WITS 2020

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The success of any words-characters recognition system depends on board parameters such as the language (Arabic, Latin, Indi …), the document type (writing or typing), based or free-segmentation, pretreatment, features extraction and classification approaches. Within these fields, Building a robust and viable recognition system for Arabic handwritten has always been a challenging task since a long time. In this study, we propose an end-to-end system based on deep Convolutional Recurrent Neural Network CNN/RNN; we trained our system on IFN/ENIT extended database in order to improve our results.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel CNN-Based Obstacle Avoidance Using RGB-Depth Image Fusion

Nächstes Kapitel Tuning Image Descriptors and Classifiers: The Case of Emotion Recognition

Al-Kabi MN, Al-Ayyoub M, Wahsheh HA, Alsmadi I (2016) A prototype for a standard Arabic sentiment analysis corpus. Int Arab J Inf Technol 13(1A):163–170

Alayba AM, Palade V, England M, Iqbal R (2017) Arabic language sentiment analysis on health services. https://arxiv.org/abs/1702.03197

Akram B (2018) Clonal selection classification algorithm applied to Arabic writer identification. In: 8th international conference on off-line handwriting based writer identification. https://doi.org/10.1145/3200842.3208087

Ibtissame A, Ammour A, Khaissidi G, Belahsen F, Mrabti M, Aboulem G (2020) A novel approach combining temporal and spectral features of Arabic online handwriting for Parkinson’s disease prediction. J Neurosci Methods. https://doi.org/10.1016/j.jneumeth.2020.108727CrossRef

El Choubassi MM, El Khoury HE, Alagha CEJ, Skaf JA, Al-Alaoui MA (2003) Arabic speech recognition using recurrent neural networks. In: Proceedings of the 3rd IEEE international symposium on signal processing and information technology, pp 543–547. https://doi.org/10.1109/ISSPIT.2003.1341178

Shi B, Bai X, Yao C (2016) An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans Pattern Anal Mach Intell 39:2298–2304. https://doi.org/10.1109/TPAMI.2016.2646371CrossRef

Elbashir MK, Mustafa ME (2018) Convolutional neural network model for Arabic handwritten characters recognition. Int J Adv Res Comput Commun Eng 7(11). https://doi.org/10.17148/IJARCCE.2018.7111

Tagougui N, Boubaker H, Kherallah M, Alimi AM (2014) A hybrid NN/HMM modeling technique for online Arabic handwriting recognition. CoRR (Online) https://arxiv.org/abs/1401.0486

Younis KS (2017) Arabic handwritten character recognition based on deep convolutional neural networks. Jordanian J Comput Inf Technol 3(3). https://doi.org/10.5455/jjcit.71-1498142206

10.

Torki M et al (2014) Window-based descriptors for Arabic handwritten alphabet recognition: a comparative study on a novel dataset. arXiv preprint arXiv:1411.3519

11.

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

12.

Sahlol A, Suen C (2013) A novel method for the recognition of isolated handwritten Arabic characters. Technical report. Cornell University

13.

Alamri H, Sadri J, Suen CY, Nobile N (2008) A novel comprehensive database for arabic off-line handwriting recognition. In: 11th international conference on frontiers in handwriting recognition 2008. Montreal

14.

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

15.

Mudhsh M, Almodfer R (2017) Arabic handwritten alphanumeric character recognition using very deep neural network. Information 8(3):105. https://doi.org/10.3390/info8030105CrossRef

16.

Abdelazeem S, El-Sherif E (2017) The Arabic handwritten digits databases ADBase & MADBase. Available online: https://datacenter.aucegypt.edu/shazeem/. Accessed on 24 Aug 2017

17.

Lawgali A, Angelova M, Bouridane A (2013) HACDB: handwritten Arabic characters database for automatic character recognition. In: 4th European workshop on visual information processing (EUVIP), pp 255–259. https://doi.org/10.4108/eai.18-7-2019.2287842

18.

El-Sawy A, EL-Bakry H, Loey M (2016) CNN for handwritten Arabic digits recognition based on LeNet-5. In: Proceedings of the international conference on advanced intelligent systems and informatics 2016, pp 566–575. https://doi.org/10.1007/978-3-319-48308-5

19.

Elleuch M, Maalej R, Kherallah M (2016) A new design based-SVM of the CNN classifier architecture with dropout for offline Arabic handwritten recognition. Proc Comput Sci 80:1712–1723. https://doi.org/10.1016/j.procs.2016.05.512CrossRef

20.

Elleuch M, Tagougui N, Kherallah M (2016) A novel architecture of CNN based on SVM classifier for recognizing Arabic handwritten script. Int J Intell Syst Technol Appl 15(4):323–340. https://doi.org/10.1504/IJISTA.2016.10000779CrossRef

21.

Yin F, Wang QF, Zhang XY, Liu CL (2013) ICDAR Chinese handwriting recognition competition. In: 2013 12th ICDAR on document analysis and recognition

22.

Lawgali A, Angelova M, Bouridane A (2013) HACDB: handwritten Arabic characters database for automatic character recognition. In: 2013 4th European workshop on visual information processing (EUVIP), pp 255–259. https://doi.org/10.4108/eai.18-7-2019.2287842

23.

Mishra A, Alahari K, Jawahar CV (2012) Scene text recognition using higher order language priors. BMVC. https://doi.org/10.5244/C.26.127CrossRef

24.

Wang K, Babenko B, Belongie S (2011) End-to-end scene text recognition. In: ICCV, 2011. https://doi.org/10.1109/ICCV.2011.6126402

25.

Lucas SM, Panaretos A, Sosa L, Tang A, Wong S, Young R, Ashida K, Nagai H, Okamoto M, Yamamoto H, Miyao H, Zhu J, Ou W, Wolf C, Jolion J (2003) ICDAR 2003 robust reading competitions: entries, results, and future directions. IJDAR. https://doi.org/10.1109/ICDAR.2003.1227749

26.

Alsaeedi A, Al Mutawa H, Natheer S, Al Subhi W, Snoussi S, Omri K (2018) Arabic words recognition using CNN and TNN on a smartphone. In: IEEE 2nd international workshop on Arabic and derived script analysis and recognition, pp 57–61. https://doi.org/10.1109/ASAR.2018.8480267

27.

Yousfi B (2016) Contribution of recurrent connectionist language models in improving lstm-based Arabic text recognition in videos. Pattern Recogn. https://doi.org/10.1016/j.patcog.2016.11.011CrossRef

28.

Mohsin A (2020) Developing an Arabic handwritten recognition system by means of artificial neural network. J Eng Appl Sci. https://doi.org/10.36478/jeasci.2020.1.3CrossRef

29.

Schäfer AM, Udluft S, Zimmermann HG (2006) Learning long term dependencies with recurrent neural networks. In: Proceedings of the 16th international conference on artificial neural networks (ICANN 2006), vol 4131. https://doi.org/10.1007/11840817_8

30.

Bengio Y, Simard PY, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Networks 5(2):157–166. https://doi.org/10.1109/72.279181CrossRef

31.

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735CrossRef

32.

Graves A, Fernández S, Gomez F, Schmidhuber J (2006) Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on machine learning, vols 12, 13, 16 and 20. ACM, pp 369–376. https://doi.org/10.1145/1143844.1143891

33.

Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673–2681CrossRef

34.

Taylor C, Rogers G, Hughes Z, Ceyhun O, MatLab vs. Python vs. R (2017) Business faculty publications. 51. https://scholar.valpo.edu/cba_fac_pub/51

35.

Abadi M, Agarwal A (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467v2

36.

Yan R, Peng L, Xiao S, Johnson MT, Wang S (2019) Dynamic temporal residual network for sequence modeling. Int J Doc Anal Recogn (IJDAR). https://doi.org/10.1007/s10032-019-00328-xCrossRef

37.

Awni M, Khalil MI, Abbas HM (2019) Deep-learning ensemble for offline Arabic handwritten words recognition. In: 2019 14th international conference on computer engineering and systems (ICCES). https://doi.org/10.1109/icces48960.2019.9068184

38.

Maalej R, Kherallah M (2019) Maxout into MDLSTM for offline Arabic handwriting recognition. In: Gedeon T, Wong K, Lee M (eds) Neural information processing. ICONIP 2019. Lecture notes in computer science. Cham. https://doi.org/10.1007/978-3-030-36718-3_45

Titel: Arabic Handwriting Word Recognition Based on Convolutional Recurrent Neural Network
verfasst von: Manal Boualam
Youssef Elfakir
Ghizlane Khaissidi
Mostafa Mrabti
Verlag: Springer Singapore
Buch: WITS 2020
Print ISBN: 978-981-336-892-7

Electronic ISBN: 978-981-336-893-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-981-33-6893-4_79

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Nachhaltigkeitsaward Key Visual/© Cometis AG/Global ESG Monitor | Daniel Rupp | Generiert mit KI, Search Icon, Banner Hanser, Jonas Klose/© Pine Valley Capital GmbH, Carina Kießling von der Strategieberatung Roland Berger/© Monika Walther Fotografie | ATZ, Beijing Auto Show 2024: Deutsche Hersteller wollen angreifen./© EKH-Pictures / Generated with AI / Stock.adobe.com, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, Zukunftswerkstatt Sales Excellence 2024/© AndreyPopov / Getty Images / iStock, 2023_Antrieb/© supervisuell, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Neuer Inhalt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.