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Utilizing Optical Character Recognition and Boarder Detection Algorithms to Identify Trading Cards

9 pagesPublished: September 27, 2019

Abstract

Magic: The Gathering is a popular physical trading card game played by millions of people around the world. To keep track of their cards, players typically store them in some sort of physical protective case, which can become cumbersome to sort through as the number of cards can reach up to the thousands. By utilizing and improving optical character recognition software, the TCG Digitizer allows users to efficiently store their entire inventory of Magic: The Gathering trading cards in a digital database. With an emphasis on quick and accurate scanning, the final product provides an intuitive digital solution for storing Magic: The Gathering cards for both collectors and card owners who want to easily store their collection of cards on a computer.

Keyphrases: AForge, AForge.NET, blob detection, Boarder Detection, Bradley Local Thresholding, edge detection, Google, magic, Magic The Gathering, OCR, Optical Character Recognition, Postgres, PostgreSQL, Quadrilateral Transformation, Tesseract

In: Frederick C. Harris Jr, Sergiu Dascalu, Sharad Sharma and Rui Wu (editors). Proceedings of 28th International Conference on Software Engineering and Data Engineering, vol 64, pages 165--173

Links:
BibTeX entry
@inproceedings{SEDE2019:Utilizing_Optical_Character_Recognition,
  author    = {Brodie Boldt and Christopher Cooper and Ryan Fox and Jared Parks and Erin Keith},
  title     = {Utilizing Optical Character Recognition and Boarder Detection Algorithms to Identify Trading Cards},
  booktitle = {Proceedings of 28th International Conference on Software Engineering and Data Engineering},
  editor    = {Frederick Harris and Sergiu Dascalu and Sharad Sharma and Rui Wu},
  series    = {EPiC Series in Computing},
  volume    = {64},
  pages     = {165--173},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/QT9R},
  doi       = {10.29007/qkhd}}
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