Development of Handwriting Recognition System in Postal Service Sector

Authors

  • E. O. Y. Ngu Faculty of Electrical Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor
  • S. H. A. Ali Faculty of Electrical Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor

Keywords:

Handwriting Recognition, K-Nearest Neighbor, Postal Service Sector,

Abstract

Handwriting recognition is a comparatively popular research due to its diverse applicable environment. It helps to solve complicated problems and at the same time, it reduces manpower consumption. This paper proposes a system for recognizing online handwritten characters by using KNearest Neighbor (KNN). General steps of an algorithm are: (1) capturing the postcode and name of district area by using external web camera, (2) performing image processing on the image, (3) creating input data for KNN by extracting vital feature from each character, (4) classifying the dataset using KNN algorithm and performing recognition during the test, and (5) providing result of the recognition. The experiment was carried out in the aspect of text font size, the density of text and light intensity of background text. Experiment results show that training sets, trained inputs and untrained inputs achieved reasonably good result with an accuracy rate of 100%, 87.54% and 75.35% respectively. For processing time, the training sets consumed the lowest processing time which is 195.32ms, followed by trained inputs with 201.30ms and untrained inputs with 204.98ms. Additionally, medium font size, high-density text and optimum intensity of the background text managed to achieve high accuracy rate and low processing time. In this way, the system is able to help the postal services sector to speed up the sorting process as well as reducing manpower consumption in the sorting unit at the same time. Overall, the system has fulfilled the objective of the project, which is to propose high accuracy and short processing time of the handwriting recognition system.

References

P.S. Wang, Character & Handwriiting Recognition, USA: World Scientific, USA, 2014.

Malaysian Digest, Malaysia Ranked Third in Mobile Shopping Growth In Asia Pacific, Are We Addicted to Shopping Apps? [Online]. Available from: http://malaysiandigest.com/frontpage/282-maintile/575054-malaysia-ranked-third-in-mobile-shopping-growth-in asia-pacific-are-we-addicted-to-shopping-apps.html. [Access from 30th November 2016].

S. C. Chan, “Handwritten Capital Letter Recognition using Neural Network,” Bachelor thesis, University Tun Hussein Onn Malaysia (UTHM), 2015.

Z. Q. Liu, J. Cai and B. Richard. Handwriting Recognition Soft Computing and Probabilistic Approach, Germany: Springer, 2016.

P. Krishna and G. Vinit, “Review on Handwritten Digits Recognition System,” International Journal of Advance Research in Computer Science and management Studies, 2015, pp. 94-101.

Wan Zulkifli Wan Ngah @ W.Yahya, “Handwriting Recognition System for Data Entry,” Bachelor thesis, University Tun Hussein Onn Malaysia (UTHM), 2014.

A.Desai, N. Bhavikatti and R.Patil, “Design and Simulation of Handwritten Text Recognition System,” .International Journal of Current Engineering and Technology, 2013, pp. 259-262.

V. Neiger, “Handwritten digits recognition using OpenCV,” Final project of Machine Learning in Computer Vision, 2015), pp. 1-11.

D. Sujitha, “To Analysis of a Handwriting Recognition Using KNN, NN and Decision Tree Classifiers,” International Journal of Computer Science and Mobile Computing, 2015, pp. 351-357.

S. Impedovo and M.Sebastiano, Fundamentals in Handwriting Recognition, Germany: Springer, 2015.

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Published

2017-11-30

How to Cite

Ngu, E. O. Y., & Ali, S. H. A. (2017). Development of Handwriting Recognition System in Postal Service Sector. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-7), 119–123. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3085