Skip to main content
SpringerLink
Log in

Multi Language Recognition Translator App Design Using Optical Character Recognition (OCR) and Convolutional Neural Network (CNN)

  • Conference paper
  • First Online:
Data Science and Emerging Technologies (DaSET 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 165))

Included in the following conference series:

Abstract

A mobile translator is a Phone’s app that lets user to translate between languages. In this paper, we proposed multilanguage recognition translator (MLRT) mobile app to help the education system, especially for students who are new to Arabic, Malay, and English to learn the languages and translated to other languages. OCR Methodology has been chosen for this project because it is the most appropriate methodology to develop a mobile application. Data acquisition, pre-processing, segmentation, feature extraction, classification, and post-processing are the six phases for OCR methodology. The Convolutional Neural Network (CNN) algorithm is used by deep learning to identify objects in image and Optical Character Recognition (OCR) is used for feature extraction to process Arabic words and translate them into Malay. A system architecture has been created to provide an overview of how the application will run and the functionality and the framework of output to show the application works.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abubaker, A.A., Lu, J.: The optimum font size and type for students aged 9–12 reading Arabic characters on screen: a case study. J. Phys. Conf. Ser. 364(1) (2012). https://doi.org/10.1088/1742-6596/364/1/012115

  2. Akouaydi, H., Abdelhedi, S., Njah, S., Zaied, M., Alimi, A.M.: Decision trees based on perceptual codes for on-line Arabic character recognition. In: 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017, pp. 153–157 (2017). https://doi.org/10.1109/ASAR.2017.8067778

  3. Alsaeedi, A., Al Mutawa, H., Natheer, S., Al Subhi, W., Snoussi, S., Omri, K.: Arabic words recognition using CNN and TNN on a Smartphone. In: 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018, 57–61 (2018). https://doi.org/10.1109/ASAR.2018.8480267

  4. Althobaiti, H., Lu, C.: A survey on Arabic optical character recognition and an isolated handwritten Arabic character recognition algorithm using encoded freeman chain code. In: 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017, pp. 1–6 (2017). https://doi.org/10.1109/CISS.2017.7926062

  5. Alwaqfi, Y.M., Mohamad, M.: A review of Arabic optical character recognition techniques & performance. Int. J. Eng. Trends Technol. 1, 44–51 (2020). https://doi.org/10.14445/22315381/CATI1P208

  6. Azeem, S.A., Ahmed, H.: Recognition of segmented online Arabic handwritten characters of the ADAB database. In: Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, vol. 1, pp. 204–207 (2011). https://doi.org/10.1109/ICMLA.2011.120

  7. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM T. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

  8. Guo, G., Li, S., Chan, K.: Character recognition by support vector machines. In: IEEE International Conference an Automatic Face and Gesture Recognition, pp. 196–201 (2000)

    Google Scholar 

  9. Goswami, R., Sharma, O.P.: A review on character recognition techniques. Int. J. Comput. Appl. 83(7), 18–23 (2013). https://doi.org/10.5120/14460-2737

    Article  Google Scholar 

  10. Hamdani, M., Mousa, A.E.D., Ney, H.: Open vocabulary arabic handwriting recognition using morphological decomposition. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 280–284 (2013). https://doi.org/10.1109/ICDAR.2013.63

  11. Papageorgiou, C.: A trainable system for object detection. Int. J. Comput. Vision 18, 1 (2000)

    MATH  Google Scholar 

  12. Pashte, P., Samir Kerawdekar, P.B.: A review on OCR methodology Rajendra Mane College of engineering and technology, Ambav, Ratnagiri, India. 5(02), 1049–1050 (2017)

    Google Scholar 

  13. Gadekar, R.R1, Bhosale, R.S2: K-Nearest neighbor classification over encrypted relational data. 2(4), 704–708 (2016). IJARIIE-ISSN(O)-2395-4396

    Google Scholar 

  14. Sahu, N., Sonkusare, M.: A study on optical character recognition techniques. Int. J. Comput. Sci. Inf. Technol. Control Eng. 4(1), 01–15 (2017). https://doi.org/10.5121/ijcsitce.2017.4101

    Article  Google Scholar 

  15. Savita, A., Amit, C., Anand, N., Saurabh, S., Byungun, Y.: Improved handwritten digit recognition using convolutional neural network (CNN). Sensors (2020). MDPI

    Google Scholar 

  16. Tappert, C., Suen, C., Wakahara, T.: The state of the art in online handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12, 8 (1990)

    Article  Google Scholar 

  17. Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural network applied to visual document analysis. In: Sevent International Conference on Document Analysis and Recognition. IEEE (2003)

    Google Scholar 

  18. Abhishek, B.V.S., Yamuna, K., Anjali, T.: Multilingual translational optical character recognition system for printed Telugu text. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–5. IEEE (2021)

    Google Scholar 

  19. Mushtaq, F., Misgar, M.M., Kumar, M., Khurana, S.S.: UrduDeepNet: offline handwritten Urdu character recognition using deep neural network. Neural Comput. Appl. 33(22), 15229–15252 (2021). https://doi.org/10.1007/s00521-021-06144-x

    Article  Google Scholar 

  20. Kataria, B., Jethva, D.H.B.: CNN-bidirectional LSTM based optical character recognition of sanskrit manuscripts: a comprehensive systematic literature review. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol. (IJSRCSEIT) (2019). ISSN, 2456–3307

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UNITAR International University, Petaling Jaya, Selangor, Malaysia

    Mohamad Khairul Naim Zulkifli, Paridah Daud & Normaiza Mohamad

Authors
  1. Mohamad Khairul Naim Zulkifli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paridah Daud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Normaiza Mohamad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paridah Daud .

Editor information

Editors and Affiliations

  1. UNITAR Graduate School, UNITAR International University, Selangor, Malaysia

    Yap Bee Wah

  2. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

  3. Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia

    Azlinah Mohamed

  4. School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zulkifli, M.K.N., Daud, P., Mohamad, N. (2023). Multi Language Recognition Translator App Design Using Optical Character Recognition (OCR) and Convolutional Neural Network (CNN). In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation