Smart receipt system - image processing, data analytics and server development

The Smart Receipt system(SRS) was developed to create an easy way for users to keep track of daily expenditure. The SRS comprises of multiple sub systems which includes development of a mobile application, back-end server, and a database which uses the Google’s Firebase. The idea was to use techniq...

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Main Author: Tham, Guo Bin
Other Authors: Ng Wee Keong
Format: Final Year Project
Language:English
Published: 2018
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Online Access:http://hdl.handle.net/10356/73899
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-738992023-03-03T20:36:14Z Smart receipt system - image processing, data analytics and server development Tham, Guo Bin Ng Wee Keong School of Computer Science and Engineering DRNTU::Engineering The Smart Receipt system(SRS) was developed to create an easy way for users to keep track of daily expenditure. The SRS comprises of multiple sub systems which includes development of a mobile application, back-end server, and a database which uses the Google’s Firebase. The idea was to use techniques like Optical Character recognition(OCR), and pre-image processing to process receipt images and convert it to digital information so users can keep track of their expenditure with ease. Pre-image processing technique’s main aim was to improve image quality to improve OCR recognition rate. In addition, the use of Naïve Bayes classification to automatically classify receipt items into different types of categories for user to view how much they spend on each type of item. Lastly, the implementation of Rule association mining to identify user’s buying pattern and recommend different kind of items according to their buying habits. The SRS was deployed and tested within Nanyang Technological University by deploying it to Cyber Security Lab’s server. The mobile application was deployed to Google Play store for users to test and use the financial application. Lastly, further improvements can be made to improve the Naïve Bayes classifier by adding a self-learning module to add new categories to the classifier. Also, improvements can be made to the OCR engine to improve the recognition rate. Bachelor of Engineering (Computer Science) 2018-04-19T02:49:22Z 2018-04-19T02:49:22Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/73899 en Nanyang Technological University 68 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Tham, Guo Bin
Smart receipt system - image processing, data analytics and server development
description The Smart Receipt system(SRS) was developed to create an easy way for users to keep track of daily expenditure. The SRS comprises of multiple sub systems which includes development of a mobile application, back-end server, and a database which uses the Google’s Firebase. The idea was to use techniques like Optical Character recognition(OCR), and pre-image processing to process receipt images and convert it to digital information so users can keep track of their expenditure with ease. Pre-image processing technique’s main aim was to improve image quality to improve OCR recognition rate. In addition, the use of Naïve Bayes classification to automatically classify receipt items into different types of categories for user to view how much they spend on each type of item. Lastly, the implementation of Rule association mining to identify user’s buying pattern and recommend different kind of items according to their buying habits. The SRS was deployed and tested within Nanyang Technological University by deploying it to Cyber Security Lab’s server. The mobile application was deployed to Google Play store for users to test and use the financial application. Lastly, further improvements can be made to improve the Naïve Bayes classifier by adding a self-learning module to add new categories to the classifier. Also, improvements can be made to the OCR engine to improve the recognition rate.
author2 Ng Wee Keong
author_facet Ng Wee Keong
Tham, Guo Bin
format Final Year Project
author Tham, Guo Bin
author_sort Tham, Guo Bin
title Smart receipt system - image processing, data analytics and server development
title_short Smart receipt system - image processing, data analytics and server development
title_full Smart receipt system - image processing, data analytics and server development
title_fullStr Smart receipt system - image processing, data analytics and server development
title_full_unstemmed Smart receipt system - image processing, data analytics and server development
title_sort smart receipt system - image processing, data analytics and server development
publishDate 2018
url http://hdl.handle.net/10356/73899
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