ELECTRONIC ONLINE HANDWRITING CHARACTER RECOGNITION SYSTEM USING ARDUINO PLATFORM

Authors

  • A. R. Syafeeza Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Albert Ngan Ban Chew Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Muhammad Muhaimin Hilmi Sadari Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Mohamad Nasriq Kamaruddin Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Wong Jia Li Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia
  • Zarini Md Zauber Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), 76100 Durian Tunggal, Melaka, Malaysia

DOI:

https://doi.org/10.11113/jt.v79.10229

Keywords:

Handwriting recognition system, Arduino mega board, Arduino TFT touch screen, XOR bit-wise, character

Abstract

This paper proposes an online prototype approach of handwriting character recognition system using a microcontroller. The main contribution of this work is the simple method of translating a handwriting input from analog to a digital font using XOR bit-wise operation. This system recognizes characters and numbers in natural handwriting with a stylus. This electronic system is used to improve the technology from the use of a bulky keyboard system to a portable and convenient way which is more suitable for smaller electronic devices nowadays such as smart phone, tablet and etc. In order to construct the system, a Arduino Mega is used as the microcontroller along with the Arduino TFT Touch Shield and an LCD screen as the input and output respectively as the hardware components. For the software part, this system uses C language and Arduino software (IDE). The recognition result achieved is 80.0%.

References

Priyanka.M, Ramakrishanan, S., Raajan, N.R. 2013. Electronic Handwriting Character Recognition (E-Hwcr). International Journal of Engineering Aand Technology (IJET). 5(3).

K. P. Ambika, U. Ramesh, K. Saravanan, and J. Hencil Peter. 2013. An Enhanced Version of Pattern Matching Algorithm using Bitwise XOR Operation. International Journal of Computer Applications. 68(23): 0975-8887.

Wei, T. J. 2011. Handwriting Recognition System. Faculty of Electrical Engineering. Malaysia, Universiti Teknologi Malaysia. Bachelor of Electrical Engineering (Computer). 62.

Rachana R. Herekar, Prof. S. R. Dhotre, 2014. Handwritten Character Recognition Based on Zoning Using Euler Number for English Alphabets and Numerals. IOSR Journal of Computer Engineering (IOSR-JCE). 16(4): 75-88.

Olarik Surinta, Lambert Schomaker and Marco Wiering. 2013. A Comparison of Feature and Pixel-Based Methods for Recognizing Handwritten Bangla Digits. 12th International Conference on Document Analysis and Recognition, 2013. Washington, DC. 25-28 Aug. 2013. 165-169.

Farha Mendi, Srinivasa.G, Ashwini A.J and Hemanth Krishna H.K. 2013. Online Hand Written Character Recognition. IOSR Journal of Computer Engineering (IOSR-JCE). 11(5): 30-36.

J. Pradeep, E. Srinivasan and S. Himavathi. 2011. Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System Using Neural Network. International Journal of Computer Science & Information Technology (IJCSIT). 3(1).

Radzi, S.A. and M. Khalil-Hani. Character Recognition Of License Plate Number Using Convolutional Neural Network. International Visual Informatics Conference. 2011. Springer.

Saad, N., et al. 2017. Real-Time LCD Digit Recognition System. Indonesian Journal of Electrical Engineering and Computer Science. 6(2).

Patil, V. and S. Shimpi. 2011. Handwritten English Character Recognition Using Neural Network. Elixir Comput Sci Eng. 41: 5587-5591.

Itqan, K. et al. 2016. User Identification System Based On Finger-Vein Patterns Using Convolutional Neural Network. ARPN Journal of Engineering and Applied Sciences. 11(5): 3316-3319.

Syafeeza, A., et al. 2015. Convolutional Neural Networks with Fused Layers Applied to Face Recognition. International Journal of Computational Intelligence and Applications. 14(03): 1550014.

Liew, S. S., et al. 2016. Gender Classification: A Convolutional Neural Network Approach. Turkish Journal of Electrical Engineering & Computer Sciences. 24(3): 1248-1264.

Syafeeza, A., et al. 2017. Design of Finger-vein Capture Device with Quality Assessment using Arduino Micrcontroller. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 9(1): 55-60.

Lengare, P. S. and M. E. Rane. 2015. Human Hand Tracking Using MATLAB to Control Arduino Based Robotic Arm. 2015 International Conference on Pervasive Computing (ICPC). IEEE.

Downloads

Published

2017-10-22

Issue

Section

Science and Engineering

How to Cite

ELECTRONIC ONLINE HANDWRITING CHARACTER RECOGNITION SYSTEM USING ARDUINO PLATFORM. (2017). Jurnal Teknologi (Sciences & Engineering), 79(7). https://doi.org/10.11113/jt.v79.10229