Analisis Perbandingan Optical Character Recognition Google Vision dengan Microsoft Computer Vision pada Pembacaan KTP-el
DOI:
https://doi.org/10.35870/jtik.v7i4.1046Keywords:
OCR, Identity Card, Google Vision API, Microsoft Computer VisionAbstract
In this era, the need of digital data is rapidly increasing. Electronic Residental Identity Card or KTP-el is the official identity card for resident of Indonesia. One fast way to extract information on an image is by using OCR/Optical Character Recognition. Competition between Google Vision API and Microsoft Computer Vision in providing OCR service encourage companies to choose the right provider. Method conducted in this research including literature review on both OCR service provider, identification and KTP-el sample image retrieval, data grouping, code implementation and accuracy testing, result analysis and discussion, and conclusion. The result of this research show that Microsoft Computer Vision have better accuracy in reading characters in KTP-el with an accuracy percentage of 0,81% to 15,8% difference to Google Vision. Google Vision has competitive accuracy, but suffers from deficiencies when reading KTP-el with blur and noise.
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Thammarak, K., Sirisathitkul, Y., Kongkla, P. and Intakosum, S., 2022. Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API. Civil Engineering Journal, 8(7), pp.1447-1458. DOI: https://doi.org/10.28991/CEJ-2022-08-07-09.
Guntara, R.G., 2022. Aplikasi Pengenalan Citra Wajah di KTP Menggunakan Google Cloud Vision API dan Kairos API Berbasis Android. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 4(2), pp.198-207. DOI: https://doi.org/10.28926/ilkomnika.v4i2.504.
Toha, M.R. and Triayudi, A., 2022. PENERAPAN MEMBACA TULISAN DI DALAM GAMBAR MENGGUNAKAN METODE OCR BERBASIS WEBSITE (STUDI KASUS: e-KTP). JST (Jurnal Sains dan Teknologi), 11(1), pp.175-183. DOI: https://doi.org/10.23887/jstundiksha.v11i1.42279.
Thammarak, K., Kongkla, P., Sirisathitkul, Y. and Intakosum, S., 2022. Comparative analysis of Tesseract and Google Cloud Vision for Thai vehicle registration certificate. International Journal of Electrical and Computer Engineering, 12(2), pp.1849-1858.
ElHanafi, A.M., Nurmadi, R., Tommy, T. and Siregar, R., 2020. Pemaparan Teknologi Biometrika Dan Google Cloud Vision Api Di SMK Dwi Tunggal 2 Tanjung. Jurnal TUNAS, 1(2), pp.62-65. DOI: http://dx.doi.org/10.30645/jtunas.v1i2.15.
Takapente, C.B., Sompie, S.R. and Poekoel, V.C., 2018. Implementasi Azure Cognitive Service Untuk Aplikasi Pengkategorian Foto. Jurnal Teknik Informatika, 13(4), pp. 1-8. DOI: https://doi.org/10.35793/jti.13.4.2018.28093.
G. Sugiarta, D. P. Andini, and S. Hidayatullah, 2021. Ekstraksi Informasi/Data e-KTP Menggunakan Optical Character Recognition Convolutional Neural Network. JTERA (Jurnal Teknologi Rekayasa), 6(1), pp. 1-6.
Pathak, A., Ruhela, A., Saroha, A.K. and Bhardwaj, A., 2019. Examining Robustness of Google Vision API Based on the Performance on Noisy Images. International Journal of Computer Science and Engineering (JCSE), 7(3), pp. 89-93, 2019.
Mangundap, J.J., Tasripan, T. and Kusuma, H., 2022. Sistem Pengenalan Text Pada Kemasan Obat untuk Membantu Penyandang Tunanetra dengan Keluaran Suara. Jurnal Teknik ITS, 11(2), pp.A128-A133. DOI: http://dx.doi.org/10.12962/j23373539.v11i2.90393.
Ruspandi, R.B., Sompie, S.R. and Kambey, F.D., 2018. Implementasi Azure Cognitive Service untuk Aplikasi Speech Recognition. Jurnal Teknik Informatika, 13(4), pp. 1-10. DOI: https://doi.org/10.35793/jti.13.4.2018.28091.
El Maghraby, A., 2021. Improving Custom Vision cognitive services model. Journal of the ACS, 11. 12(1), pp. 36-63.
Muhammad, F.D., 2021. Penggunaan e-KTP untuk Registrasi Otomatis Memanfaatkan Sistem OCR Dengan Metode Template Matching Correlation. Media Jurnal Informatika, 12(2), pp.57-60.
Purba, A.M., Harjoko, A. and Wibowo, M.E., 2019. Text Detection In Indonesian Identity Card Based On Maximally Stable Extremal Regions. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 13(2), pp.177-188. DOI: https://doi.org/10.22146/ijccs.41259.
Putri, D.Z., Puspitaningrum, D. and Setiawan, Y., 2018. Konversi Citra Kartu Nama ke Teks Menggunakan Teknik OCR dan Jaro-Winkler Distance. Jurnal Teknoinfo, 12(1), pp.1-6. DOI: https://doi.org/10.33365/jti.v12i1.35.
Susanty, M. and Nugroho, H., 2020. Optical Character Recognition Implementation for Admission System in Universitas Pertamina. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 11(1), pp.165-170. DOI: https://doi.org/10.24176/simet.v11i1.3838.
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