Total views : 169

A Research on Constructing Consumption Pattern Analysis System through Purchase Records


  • Department of IT Engineering, Kongju National University, 31080, Korea, Republic of
  • Department of IT Convergence, Woosong University, 34606, Korea, Republic of


Objectives: To understand the economic indicator through purchase records. Methods/Statistical Analysis: In this thesis, a system analyzing the economic indicator of the individuals is proposed. The purchase information such as receipt can be either directly entered into the application by the users or entered into the application through the actualized optical character recognition technique. The purchase records such as receipt of the individuals can be entered into the application to be saved in the database and the saved data can be analyzed to analyze the economic activities conducted by the individuals. Since such system can be used to confirm the amount of money spent as well as the particular field the consumption leans toward, the individuals are able to set up their future economic plan. Findings: The proposed system visualizes the detailed information of the economic activities conducted by the individuals based on its database in which diverse purchase records such as receipts that occur during the economic activities conducted by the individuals are saved. The preexisting researches were focused on using the data-mining technique to develop a system that recommends products to customers by predicting their purchase intention. However, most of these researches were conducted to support companies to promote efficient marketing and therefore were unsuitable for providing the integrated economic indicator to the individuals. Accordingly, the proposed system can be used to analyze diverse consumption patterns based on the purchase records of the users and the analyzed result can be used as an important indicator of the individuals’ economic activities. Improvements/Applications: The proposed system can be valuably used to understand the economic indicator of the individuals. In the future research, the accuracy of the optical character recognition must be studied and the big data analysis must be studied as well.


Consumer, Consumption Plan, OCR, Purchase, Receipt.

Full Text:

 |  (PDF views: 152)


  • Seo GD, Lee JE. A study on the effect of consumer lifestyle on brand attitude, brand attachment influence upon brand loyalty. Journal of Digital Convergence. 2016; 14(4):185–92.
  • Vijay AV. A study on consumer brand preference with reference to DTH in rural area. Indian Journal of Science and Technology. 2016 Jul; 9(27):1–5.
  • Yang BH. A link between consumer empathy and brand attachment on branded mobile Apps: The moderating effect of ideal self-congruence. Indian Journal of Science and Technology. 2016 Jul; 9(25):1–9.
  • Tesseract. 2016. Available from:
  • The Tesseract OCR Engine. 2007. Available from: en//pubs/archive/33418.pdf
  • Go EB, Ha YJ, Choi SR, Lee KH, Park YH. An implementation of an android mobile system for extracting and retrieving texts from images. Journal of Digital Contents Society. 2011; 12(1):57–67.
  • Choi SR, Go EB, Ha YJ, Park YH. An implementation of a system for extracting and retrieving texts on android platform using OCR. Proceeding of Korea Multimedia Society; 2010. p. 469–73.
  • Kim JW, Kim SH, Lee CW. Construction of educational contents using on-line character recognition. Proceeding of Korea Multimedia Society; 2009. p. 309–12.
  • Singh A, Bacchuwar K, Bhasin A. A survey of OCR Applications. International Journal of Machine Learning and Computing. 2012; 2(3):314–8.
  • Mithe R, Indalkar S, Divekar N. Optical Character Recognition. International Journal of Recent Technology and Engineering. 2013; 2(1):72–5.
  • Shrivastava V, Sharma N. Artificial Neural Network based Optical Character Recognition. Signal and Image Processing: An International Journal. 2012; 3(5):73–80.
  • Kwon SK, Jo SH. Community-based travel information system using augmented reality. Journal of Korea Multimedia Society. 2015; 18(2):97–105.
  • Kanti VM. Statistics and Images. Oxford: Cartax Publishing Company; 1995.
  • Cho WH. Introduction and development of a new recognition of the Character Recognition System. Proceedings of the Spring Conference Korea Statistical Society; 1998. p.68–74.
  • Cho YY. Artificial intelligence system. Seoul: Hongrung Publishing Company; 2003.
  • Mcclell JL, Rumelhart DE. Learning internal presentation by error propagation. Parallel Distributed Processing. 1986; 1:318–62.
  • Lee SJ, Kim JH. Prediction of purchase with a receipt of the customer data and SVM. Proceeding of Korea Intelligent Information System Society; 2006 Nov. p. 179–88.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.