Smart Control of Expenses Using Mobile Devices

  • Giovani R. F. Junior UFOP
  • Vicente J. P. Amorim UFOP
  • Thiago L. Gomes UFOP
  • Igor M. Pereira UFOP

Resumo


It is estimated that only 3.1% of the Brazilian population controls their expenses through digital applications. While 8.9% does not use a digital platform due to a lack of knowledge, 8.1% do not have time. Considering the current usage levels, applications providing a more automated control of expenditures would simplify this task for an average user, making mobile applications a more attractive option. Using digital image processing techniques, optical character recognition (OCR), and post-processing a novel software was developed in Android for receipts automatic recognition allowing mobile users to monitor expenses using photos. Information recognized by the application replicated a real receipt with an acceptable level of similarity.

Referências

Administradores (2013). Para brasileiros, smartphone e tablet melhoram a qualidade de vida. http://www.administradores.com.br/noticias/tecnologia/pesquisa-para-brasileiros-smartphone-e-tablet-melhorama-qualidade-de-vida/75469/. Last accessed 2016, March 09.

Application, N. (2015). http://www.nfscan.cc/?lang=pt. Last accessed 2016, February 29.

Bassil, Y. and Alwani, M. (2012). Ocr post-processing error correction algorithm using google’s online spelling suggestion. Journal of Emerging Trends in Computing and Information Sciences.

Bruno, V. and Miret, R. (2016). 46% dos brasileiros não controlam seu orçamento, revela pesquisa do spc brasil. https://www.spcbrasil.org.br/imprensa/noticia/971-46dosbrasileirosnaocontrolamseuorcamentorevelapesquisadospcbrasil. Last accessed 2016, March 09.

Gandra, A. (2015). Spc: falta de disciplina é maior dificuldade para controle de gastos. http://agenciabrasil.ebc.com.br/economia/noticia/2015-01/falta-de-disciplina-e-principal-dificuldade-dosbrasileiros-para-nao. Last accessed 2016, February 02.

Gonzalez, R. C. and Woods, R. E. (2010). Processamento Digital de Imagens. Pearson Education, 3 edition.

Google (2016a). Android spelling checker framework. http://developer.android.com/intl/pt-br/guide/topics/text/spellchecker-framework.html. Last accessed 2016, February 29.

Google (2016b). How to use the tools provided to train tesseract 3.0x for a new language accessed 08 mar. 2016. https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract. Last accessed 2016, March 08.

Google (2016c). Mobile app marketing insights: How consumers really find and use your apps. https://think.storage.googleapis.com/docs/mobileapp-marketing-insights.pdf. Last accessed 2016, April 01.

Hasnat, M. A., Rahman, M., and Khan, C. M. (2009). An open source tesseract based optical character recognizer for bangla script. 10th International Conference on Document Analysis and Recognition.

Heng, A. (2013). L’addition: Splitting the check, made easy. https://stacks.stanford.edu/file/druid:yt916dh6570/Heng LAddition Restaurant Check Splitting.pdf. Last accessed 2016, March 09.

Levenshtein (2016). The levenshtein algorithm. http://www.levenshtein.net/. Last accessed 2016, March 02.

Library, T. O. (2016). Tesseract ocr library source code. https://github.com/tesseract-ocr. Last accessed 2016, march 01.

Qt (2016). Qt box editor tool. https://zdenop.github.io/qt-box-editor/. Last accessed 2016, march 08.

White, N. (2012). Training Tesseract for Ancient Greek OCR. Department of Classics and Ancient History 38 North Bailey Durham, DH1 3EU, UK, Email: nick.white@durham.ac.uk.

Zhel, M. (2016). 10 reasons why people abandon your app. https://mofluid.com/blog/10-reasons-why-people-abandon-your-app. Last accessed 2016, April 01.
Publicado
04/07/2016
F. JUNIOR, Giovani R.; AMORIM, Vicente J. P.; GOMES, Thiago L.; PEREIRA, Igor M.. Smart Control of Expenses Using Mobile Devices. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 43. , 2016, Porto Alegre. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2016 . p. 1633-1644. ISSN 2595-6205. DOI: https://doi.org/10.5753/semish.2016.9514.