Structured Detail Extraction from Government Documents

Authors

  • Shishir Kallapur  Department of Computer Science and Engineering, National Institute of Engineering, Mysore, India
  • Shourya Sinha  Department of Computer Science and Engineering, National Institute of Engineering, Mysore, India
  • Vinay Kumar  Department of Computer Science and Engineering, National Institute of Engineering, Mysore, India
  • Shashank Singh  Department of Computer Science and Engineering, National Institute of Engineering, Mysore, India

DOI:

https://doi.org/10.32628/CSEIT12173177

Keywords:

GUI, JSON

Abstract

Text recognition has been one of the most active and challenging research areas in the field of image processing and pattern recognition. This paper presents a method to extract various important data from government documents and store it in a JSON file and link different documents of a person so that different data can be used when required. The approach we have taken here is that first the user will have to upload the scanned copy of the document into the GUI and the data is extracted from the photo. We made sure to capture all the necessary data from the image so that even if some of the data in the image is in circular shape, the software captures all the data and from the captured data, we select the important data and store it in a JSON file.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shishir Kallapur, Shourya Sinha, Vinay Kumar, Shashank Singh, " Structured Detail Extraction from Government Documents" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.610-613, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT12173177