The Implementation of Artificial Neural Network (ANN) on Offline Cursive Handwriting Image Recognition


Fitrianingsih Fitrianingsih(1*); Diana Tri Susetianingtias(2); Dody Pernadi(3); Eka Patriya(4); Rini Arianty(5);

(1) Gunadarma University
(2) Gunadarma University
(3) Gunadarma University
(4) Gunadarma University
(5) Gunadarma University
(*) Corresponding Author

  

Abstract


Identifying a writing is an easy thing to do for human, but this does not apply to computers, in particular if it is handwriting. Handwriting recognition, especially cursive handwriting is a research in the area of image processing and pattern matching that is challenging to complete, following the different characteristics of each person's cursive handwriting style. In this study, the use of the ANN model will be implemented in performing offline handwriting image recognition. The cursive handwriting image that has been obtained is then preprocessed and segmented using bounding box rectangle and contour techniques. Evaluation of system performance using global performance metrics in this study resulted in a percentage of 93% where the bounding box and contour succeeded in determining the segmentation point correctly, so that the ANN model worked optimally.


Keywords


ANN; Global Performance Metrics; Contour; Segmentation; Writing

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 874 times
PDF view: 213 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i1.1113.63-73 		  

Cite

How to cite item

References


Fitrianingsih, S. Madenda, Ernastuti, S. Widodo, Rodiah, Cursive handwriting segmentation using ideal distance approach, International Journal of Electrical and Computer Engineering, Vol7, No.5, pp. 2863-2872, October 2017.

Fitrianingsih, S. Widodo, S. Madenda, Rodiah, Slant correction and detection for offline cursive handwriting using 2d affine transform, International Journal of Engineering Research & Technology (IJERT, ISSN: 2278-0181, International Journal of Engineering Research & Technolog, Vol 5, Issue 08, pp. 568-572, 2016.

Monika, M. Ingole, and K. Tighare, "Handwritten character recognition using neural network, " International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol 7 Issue 4, pp. 203-207, July 2021.

T.S. Gunawan, A.F.R.M. Noor, and M. Kartiwi, Development of english handwritten recognition using deep neural network, Indonesian Journal of Electrical Engineering and Computer Science, vo. 10, no 2, pp. 562-568, 2018.

A. Boukharouba, and A. Bennia, Novel feature extraction technique for the recognition of handwritten digits, Applied Computing and Informatics, Vol 13(1), pp. 19- 26, 2017.

R.G. Khalkar, A.S. Dikhit, A. Goel, and M. Gupta, "Handwritten text recognition using deep learning (CNN & RNN)," International Advanced Research Journal in Science, Engineering and Technology, Vol. 8, Issue 6, pp. 870-881, June 2021.

U. Dwivedi, P. Rajput, M.K. Sharma, and G. Noida, Cursive handwriting recognition system using feature extraction and artificial neural network, International Research Journal of Engineering and Technology, vol 4(03), pp. 2202-2206, 2017.

V. Kurkov, Y. Manolopoulos, B. Hammer, L. Iliadis, and I. Maglogiannis, Artificial neural networks and machine learning, ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, Proceedings. Basingstoke, England: Springer, 2018.

S. Aqab, and M.U. Tariq, Handwriting recognition using artificial intelligence neural network and image processing, International Journal of Advanced Computer Science and Applications, Vol. 11, No. 7, 2020.

R.R. Nair, N. Sankaran, B.U. Kota, S. Tulyakov, S. Setlur, and V. Govindaraju, Knowledge transfer using Neural network based approach for handwritten text recognition, In 2018 13th IAPR International Workshop on Document Analysis Systems (DAS) (pp. 441- 446). IEEE, 2018.

M. P. Varma, S. J. Hore, C. Uday, S. O. Reddy and V. J. Pillai, Optimized handwritten character recognition using artificial neural network, International Journal of Scientific & Technology Research, ISSN 2277-8616, Vol 9, Issue 01, January 2020.

M. Abdulllah, A. Agal, M. Alharthi, and M. Alrashidi, Retracted: Arabic handwriting recognition using neural network classifier, Journal of Fundamental and Applied Sciences, vol 10(4S), pp. 265-270, 2018.

N.A. Hamid, and N.A.A. Sjarif, Handwritten Recognition Using SVM, KNN and Neural Network, arXiv preprint arXiv:1702.00723, 2017.

U. Dwivedi, P. Rajput, and M. K. Sharma, Cursive handwriting recognition system using feature extraction and artificial neural network, International Research Journal of Engineering and Technology, vol 04, pp. 2202-2206, 2017.

Mohsin, and M. Sadoon, Developing an arabic handwritten recognition system by means of artificial neural network, Journal of Engineering and Applied Sciences, vol 15(1), pp. 13, 2020.

N. Sharma, P. Agarwal, and U. Pandey, Offline handwriting recognition using neural networks, International Journal of Advance Research, 4(2), 15411545, 2018.

N.T. Kishna, and S. Francis, Intelligent tool for malayalam cursive handwritten character recognition using artificial neural network and hidden markov model, International Conference On Inventive Computing And Informatics, pp. 595598, 2017.

K. Nahar, Offline Arabic hand-writing recognition using artificial neural network with genetics algorithm, International Arab Journal of Information Technology, vol 15(4), pp. 701707, 2018.

J. Cai, E. Sun, and Z. Chen, "OCR Service Platform Based on OpenCV", Journal of Physics: Conference Series,1883 012043, 2021.

S. Garg, K. Kumar, N. Prabhakar, and A. Ratan, Optical Character Recognition using Artificial Intelligence, International Journal of Computer Applications, vol 179(31), pp 14-20, 2018.

S. Gong, Z. Huan, M. JI, X. Chen, and Y. Bao, ITLCS based on opencv image processing technology, Journal of Physics: Conference Series, 2143 (2021) 012031, 2021.

A. Mordvintsev, and Abid K., OpenCV-Python Tutorials Documentation Release 1, [Online]. Available: https://opencv24-python-tutorials.readthedocs.io/_/downloads/en/stable/pdf/, 2021.

F. Jalled, and I. Varonkov, Object Detection using Image Processing, Nov. 2016, [Online]. Available: http://arxiv.org/abs/1611.07791, 2016.

L. H. Gong, C. Tian, W. P. Zou, and N. R. Zhou, Robust and imperceptible watermarking scheme based on Canny edge detection and SVD in the contourlet domain, Multimedia Tools and Applications, vol. 80, no. 1, pp. 439461, 2021.

T.B. Arnold, KerasR: r interface to the keras deep learning library, The Journal of Open Source Software, 2(14), pp. 296, 2017.

M. Gandhi, J. Kamdar, and M. Shah, Preprocessing of non-symmetrical images for edge detection, Augmented Human Research, vol. 5, no. 1, pp. 110, 2020.

B. Watkins and A. van Niekerk, A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery, Computers and Electronics in Agriculture, vol 158, pp. 294302, 2019.

B. Iqbal, W. Iqbal, N. Khan, A. Mahmood, and A. Erradi, Canny edge detection and hough transform for high resolution video streams using hadoop and spark, Cluster Computing, vol. 23, no. 1, pp. 397408, 2020.

J. Chaki, and N. Dey, A beginners guide to image preprocessing techniques, Boca Raton: CRC Press. ISBN: 978-1-138-33931-6, 2018.

M. Dawson, Python programming: for the absolute beginner (3rd ed), Boston, MA: Course Technology Cengage Learning. ISBN: 978-1-4354-5500-9, 2010.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Fitrianingsih Fitrianingsih

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.