Main Article Content

Abstract

A guestbook is a tool to record the identities of visitors who come to a place or event such as weddings, birthday celebrations, parties etc. Not just recording, the guestbook also functions as evidence and traces to avoid something unwanted. So it is not surprising that in some places it is required to submit an identity card such as a KTP when filling out a guestbook to be allowed to enter the place. KTP itself is an identity card that contains data such as name, place, date of birth etc. The data contained in the KTP can be utilized in the guestbook filling process so that officers only need to take a picture of the KTP and visitor data will be filled in automatically with the help of optical character recognition (OCR). To get a good OCR result, an image with clear words is needed, its position is not tilted and its size is not too small.  Therefore, various preprocessing steps are needed before performing the OCR process, one of which is by applying denoise using the Autoencoder which has succeeded in making the image cleaner and the OCR results become more accurate.

Keywords

guestbook autoencoder optical character recognition bukutamu autoencoder optical character recognition

Article Details

Author Biographies

Muhamad Aldi Rizaldi, Universitas Mercu Buana

Teknik Informatika - Fakultas Ilmu Komputer Universitas Mercu Buana

Emil R. Kaburuan, Universitas Mercu Buana

Teknik Informatika - Fakultas Ilmu Komputer Universitas Mercu Buana
How to Cite
Rizaldi, M. A., & Kaburuan, E. R. (2022). OCR Implementation with Autoencoder Method on WEB-based Guestbook Application. Jurnal Komputer Terapan , 8(2), 307–315. https://doi.org/10.35143/jkt.v8i2.5420

References

  1. R. Andarsyah and M. H. K Saputra, “Perancangan Aplikasi Digital Untuk Mencatat Data Tamu Menggunakan Arduino Uno Dan Near Field Communication (Nfc) (Studi Kasus Humas & Rekrutmen Politeknik Pos Indonesia),” Competitive, vol. 15, no. 1, 2020, doi: 10.36618/competitive.v15i1.685.
  2. F. M. Rusli, K. A. Adhiguna, and H. Irawan, “Indonesian ID Card Extractor Using Optical Character Recognition and Natural Language Post-Processing,” 2021. doi: 10.1109/ICoICT52021.2021.9527510.
  3. A. Patil, “Optical Character Recognition Implementation using Pattern Matching,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 7, no. 8, 2019, doi: 10.22214/ijraset.2019.8155.
  4. Arikunto et al., “Metode Penelitian Kuantitatif, Kualitatif, dan R&D,” J. Sci. Teacher Educ., vol. 3, no. 2, 2017.
  5. M. Mehdizadeh, C. MacNish, D. Xiao, D. Alonso-Caneiro, J. Kugelman, and M. Bennamoun, “Deep feature loss to denoise OCT images using deep neural networks,” J. Biomed. Opt., vol. 26, no. 04, Apr. 2021, doi: 10.1117/1.jbo.26.4.046003.
  6. G. Eraslan, L. M. Simon, M. Mircea, N. S. Mueller, and F. J. Theis, “Single-cell RNA-seq denoising using a deep count autoencoder,” Nat. Commun., vol. 10, no. 1, 2019, doi: 10.1038/s41467-018-07931-2.
  7. F. Zamora-Martinez, S. España-Boquera, and M. J. Castro-Bleda, “Behaviour-based Clustering of Neural Networks applied to Document Enhancement, in: Computational and Ambient Intelligence, pages 144-151, Springer, 2007.” https://archive.ics.uci.edu/ml/datasets/NoisyOffice (accessed Jul. 06, 2022).
  8. R. C. Gerum et al., “CameraTransform: A Python package for perspective corrections and image mapping,” SoftwareX, vol. 10, 2019, doi: 10.1016/j.softx.2019.100333.
  9. A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. II. doi: 10.1109/CVPR.2005.38.
  10. B. P. Pratama and S. A. Pamungkas, “Analisis Kinerja Algoritma Levenshtein Distance dalam Mendeteksi Kemiripan Dokumen Teks,” J. “LOG!K@,” vol. 6, no. 2, 2016.
  11. “KEMENDAGRI R.I.” https://www.kemendagri.go.id/arsip/detail/10857/keputusan-menteri-dalam-negeri-nomor-050145-tahun-2022-tentang-pemberian-kode-data-wilayah-administrasi-pemerintahan-dan-pulau-tahun-2021 (accessed Jul. 06, 2022).