Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 1-2/2021

17.02.2021 | Original Paper

Text recognition for Vietnamese identity card based on deep features network

verfasst von: Duc Phan Van Hoai, Huu-Thanh Duong, Vinh Truong Hoang

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 1-2/2021

Einloggen

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

search-config
loading …

Abstract

Optical character recognition (OCR) is a technology for converting text automatically on images into data strings for editing, indexing, and searching. The strings can be applied for many tasks such as to digitize old documents, translate into other languages, or to test and verify text positions. Recently, Know Your Customer (KYC) has become an industry standard for making sure that people are who they say they are. While the scope of Know Your Customer is constantly expanding, ID verification is still a crucial first step in KYC processes. Mobile OCR is one of the technological solutions that is making this part of KYC easier than ever for customers to comply with. KYC processes require financial services companies to verify the identities of their customers OCR to extract data by reading IDs, bank cards, and documents. In this paper, we investigate to develop a method for Vietnamese identity card recognition based on deep features network. On several major data fields of identity cards, it achieves an accuracy of more than 96.7% and 89.7% on character level and word level, respectively.
Vorheriger Artikel Offline script recognition from handwritten and printed multilingual documents: a survey
Nächster Artikel Persian handwritten digit, character and word recognition using deep learning

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
Fußnoten
Literatur
1.
Arafat, S.Y., Iqbal, M.J.: Urdu-text detection and recognition in natural scene images using deep learning. IEEE Access 8, 96787–96803 (2020)CrossRef
2.
Attivissimo, F., Giaquinto, N., Scarpetta, M., Spadavecchia, M.: An Automatic Reader of Identity Documents. In: 2019 IEEE International Conference on Systems. Man and Cybernetics (SMC), pp. 3525–3530. IEEE, Bari, Italy (2019)
3.
Bulatov, K., Matalov, D., Arlazarov, V.V.: MIDV-2019: challenges of the modern mobile-based document OCR. In: W. Osten, D.P. Nikolaev (eds.) Twelfth International Conference on Machine Vision (ICMV 2019), vol. 11433, pp. 717 – 722. SPIE (2020), backup Publisher: International Society for Optics and Photonics
4.
Chaudhuri, A., Mandaviya, K., Badelia, P., K Ghosh, S.: Optical Character Recognition Systems for Different Languages with Soft Computing, Studies in Fuzziness and Soft Computing, vol. 352. Springer, Cham (2017)
5.
Chen, T.H.: Do you know your customer? Bank risk assessment based on machine learning. Appl. Soft Comput. 86, 105779 (2020)CrossRef
6.
Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder–decoder approaches. In: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111. Association for Computational Linguistics, Doha, Qatar (2014)
7.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1800–1807 (2017)
8.
Clausner, C., Antonacopoulos, A., Pletschacher, S.: Efficient and effective OCR engine training. In: International Journal on Document Analysis and Recognition (IJDAR) (2019)
9.
Darwish, S.M., Elzoghaly, K.O.: An enhanced offline printed Arabic OCR model based on bio-inspired fuzzy classifier. IEEE Access 8, 117770–117781 (2020)CrossRef
10.
Deng, G., Ming, Y., Xue, J.H.: RFRN: A Recurrent Feature Refinement Network for Accurate and Efficient Scene Text Detection. Neurocomputing p. S0925231220317124 (2020)
11.
Doush, I.A., Alkhateeb, F., Gharaibeh, A.H.: A novel Arabic OCR post-processing using rule-based and word context techniques. IJDAR 21(1–2), 77–89 (2018)CrossRef
12.
Dreuw, P., Heigold, G., Ney, H.: Confidence- and margin-based MMI/MPE discriminative training for off-line handwriting recognition. IJDAR 14(3), 273–288 (2011)CrossRef
13.
Du, J., Huo, Q.: A discriminative linear regression approach to adaptation of multi-prototype based classifiers and its applications for Chinese OCR. Pattern Recogn. 46(8), 2313–2322 (2013)CrossRef
14.
Elagouni, K., Garcia, C., Mamalet, F., Sébillot, P.: Text recognition in multimedia documents: a study of two neural-based OCRs using and avoiding character segmentation. IJDAR 17(1), 19–31 (2014)CrossRef
15.
Fernández-Caballero, A., López, M.T., Castillo, J.C.: Display text segmentation after learning best-fitted OCR binarization parameters. Expert Syst. Appl. 39(4), 4032–4043 (2012)CrossRef
16.
Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural ’networks. pp. 369–376 (2006)
17.
Hoai, D.P.V., Hoang, V.T.: Feeding Convolutional Neural Network by hand-crafted features based on Enhanced Neighbor-Center Different Image for color texture classification. In: 2019 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1–6 (2019)
18.
Hoai, D.P.V., Surinwarangkoon, T., Hoang, V.T., Duong, H.T., Meethongjan, K.: A comparative study of rice variety classification based on deep learning and hand-crafted features p. 10 (2020)
19.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRef
20.
Holanda, G.B., Souza, J.W.M., Lima, D.A., Marinho, L.B., Girão, A.M., Bezerra Frota, J.B., Rebouças Filho, P.P.: Development of OCR system on android platforms to aid reading with a refreshable braille display in real time. Measurement 120, 150–168 (2018)
21.
Khosravi, P., Kazemi, E., Zhan, Q., Malmsten, J.E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S., Cooper, L.A.D., Hickman, C., Meseguer, M., Rosenwaks, Z., Elemento, O., Zaninovic, N., Hajirasouliha, I.: Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. npj Digital Medicine 2(1), 21 (2019)
22.
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2014)
23.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)CrossRef
24.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)CrossRef
25.
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1(4), 541–551 (1989)CrossRef
26.
Lee, Y., Song, J., Won, Y.: Improving personal information detection using OCR feature recognition rate. J. Supercomput. 75(4), 1941–1952 (2019)CrossRef
27.
Levenshtein, V.I.: Binary codes capable of correcting deletions insertions and reversals. Soviet Phys. Doklady 10, 707 (1966)MathSciNet
28.
Liao, M., Shi, B., Bai, X.: Textboxes++: a single-shot oriented scene text detector. IEEE Trans. Image Process. 27(8), 3676–3690 (2018)MathSciNetCrossRef
29.
Liem, H.D., Minh, N.D., Trung, N.B., Duc, H.T., Hiep, P.H., Dung, D.V., Vu, D.H.: FVI: An End-to-end Vietnamese Identification Card Detection and Recognition in Images. In: 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), pp. 338–340. IEEE, Ho Chi Minh City (2018)
30.
Lin, H., Yang, P., Zhang, F.: Review of scene text detection and recognition. Arch. Comput. Methods Eng. 27(2), 433–454 (2020)CrossRef
31.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016, pp. 21–37. Springer, Cham (2016)
32.
Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J.: Fots: Fast oriented text spotting with a unified network. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 5676–5685 (2018)
33.
Mei, J., Islam, A., Moh’d, A., Wu, Y., Milios, E.: Statistical learning for OCR error correction. Inf. Process. Manag. 54(6), 874–887 (2018)CrossRef
34.
Oni, O.J., Asahiah, F.O.: Computational modelling of an optical character recognition system for Yorùbá printed text images. Sci. Afr. 9, e00415 (2020)
35.
Piroonsup, N., Sinthupinyo, S.: Semi-supervised cluster-and-label with feature based re-clustering to reduce noise in Thai document images. Knowl.-Based Syst. 90, 58–69 (2015)CrossRef
36.
Pramanik, R., Bag, S.: Shape decomposition-based handwritten compound character recognition for Bangla OCR. J. Vis. Commun. Image Represent. 50, 123–134 (2018)CrossRef
37.
Pratama, M.O., Satyawan, W., Fajar, B., Fikri, R., Hamzah, H.: Indonesian ID Card Recognition using Convolutional Neural Networks. In: 2018 5th International Conference on Electrical Engineering. Computer Science and Informatics (EECSI), pp. 178–181. IEEE, Malang, Indonesia (2018)
38.
Puigcerver, J.: Are multidimensional recurrent layers really necessary for handwritten text recognition? In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 01, pp. 67–72 (2017)
39.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, pp. 234–241. Springer, Cham (2015)
40.
Shi, B., Bai, X., Yao, C.: 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(11), 2298–2304 (2017)CrossRef
41.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
42.
Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision - ECCV 2016, pp. 56–72. Springer, Cham (2016)
43.
Yin, Y., Zhang, W., Hong, S., Yang, J., Xiong, J., Gui, G.: Deep learning-aided OCR techniques for chinese uppercase characters in the application of internet of things. IEEE Access 7, 47043–47049 (2019)CrossRef
44.
Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: East: An efficient and accurate scene text detector. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 2642–2651 (2017)
Metadaten
Titel
Text recognition for Vietnamese identity card based on deep features network
verfasst von
Duc Phan Van Hoai
Huu-Thanh Duong
Vinh Truong Hoang
Publikationsdatum
17.02.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 1-2/2021
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-021-00363-7

Weitere Artikel der Ausgabe 1-2/2021

International Journal on Document Analysis and Recognition (IJDAR) 1-2/2021 Zur Ausgabe

Editorial

Deep learning for graphics recognition: document understanding and beyond

Special Issue Paper

Arrow R-CNN for handwritten diagram recognition

Original Paper

Knowledge-driven description synthesis for floor plan interpretation

Original Paper

Combination of deep neural networks and logical rules for record segmentation in historical handwritten registers using few examples

Original Paper

Persian handwritten digit, character and word recognition using deep learning

Special Issue Paper

CNN-based segmentation of speech balloons and narrative text boxes from comic book page images