Designing a Real-Time-Based Optical Character Recognition to Detect ID Cards

Rodzan Iskandar, Mezan El Khaeri Kesuma

Abstract


This research 0aims to design a Real-time ID card detection based on Optical Character Recognition (OCR). OCR detects and records information into CSV files using a camera. Hopefully, it can become one of the administrative solutions in Indonesia by using existing identity cards using OCR in real time. This research method was carried out independently in August 2021 using ID cards as objects. The tool involved was a 320x320 pixel webcam camera on an HP Intel Core i5 7th Gen notebook. The software used by Easy OCR was Pytorch-based. ID cards were detected using an algorithm by TensorFlow object detection with SSD MobileNet V2 FPNLite 320x320 as the pre-trained model of Tensorflow. The researchers collected ID card images using a webcam with various light conditions and orientations and label them using labeling. The researchers trained it with only 20 photos. After 3000 training steps, the researchers obtained about 0.17 loss and 0.95. Thus, the ID card detection tool using OCR runs well.

Keywords


Detection; ID card; Optical Character Recognition; Realtime

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References


A. Goeritno and I. Setyawibawa, “An Electronic Device Reviewed by Diagnosing on The Modules Embodiment,” Int. J. Electron. Commun. Syst., vol. 1, no. 2, pp. 41–55, 2021, doi: 10.24042/ijecs.v1i2.10383.

M. Kesuma and R. Iskandar, “Analisis Toko dan Asal Toko Fashion Pria di Shopee Menggunakan Data Scrapping dan Exploratory Data Analysis,” Maj. Ilm. Teknol. Elektro, vol. 21, no. 1, pp. 127–134, 2022, doi: https://doi.org/10.24843/MITE.2022.v21i01.P17 Analisis.

Afdholudin, “Rancang Bangun Alat Pembaca Data E-KTP Menggunakan Library Google Vision Berbasis Raspberry PI,” Universitas Jambi (Published), 2021.

M. O. Pratama, W. Satyawan, B. Fajar, R. Fikri, and H. Hamzah, “Indonesian ID Card Recognition using Convolutional Neural Networks,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2018-Octob, pp. 178–181, 2018, doi: 10.1109/EECSI.2018.8752769.

W. Satyawan et al., “Citizen ID card Detection using Image Processing and Optical Character Recognition,” in The 3rd International Conference on Computing and Applied Informatics, 2019, vol. 1235, no. 1, doi: 10.1088/1742-6596/1235/1/012049.

A. Saepulrohman and A. Ismangil, “Data Integrity and Security of Digital Signatures on Electronic Systems using The Digital Signature Algorithm (DSA),” Int. J. Electron. Commun. Syst., vol. 1, no. 1, pp. 11–15, 2021.

W. Liu, C. Q. Cai, and Z. Zhang, “Fingerprint ID Cards and The Recognition System,” in ICCSE 2010 - 5th International Conference on Computer Science and Education, Final Program and Book of Abstracts, 2010, pp. 1219–1221, doi: 10.1109/ICCSE.2010.5593447.

M. Ryan and N. Hanafiah, “An Examination of Character Recognition on ID card using Template Matching Approach,” Procedia Comput. Sci., vol. 59, no. Iccsci, pp. 520–529, 2015, doi: 10.1016/j.procs.2015.07.534.

R. F. Harris, “Rancang Bangun Aplikasi Pengajuan Surat Keterangan Warga dengan Optical Character Recognition Berbasis Android Mobile,” Politeknik Negeri Jakarta, 2020.

J. Zhao, X. Li, C. Shum, and J. McPhee, “A Review of Physics-based and Data-Driven Models for Real-time Control of Polymer Electrolyte Membrane Fuel Cells,” Energy AI, vol. 6, p. 100114, 2021, doi: 10.1016/j.egyai.2021.100114.

S. Bian et al., “Machine Learning-based Real-time Monitoring System for Smart Connected Worker to Improve Energy Efficiency,” J. Manuf. Syst., vol. 61, no. August, pp. 66–76, 2021, doi: 10.1016/j.jmsy.2021.08.009.

R. Oucheikh, T. Pettersson, and T. Löfström, “Product Verification using OCR Classification and Mondrian Conformal Prediction,” Expert Syst. Appl., vol. 188, p. 115942, 2022, doi: 10.1016/j.eswa.2021.115942.

S. Tomovic, K. Pavlovic, and M. Bajceta, “Aligning Document Layouts Extracted with Different OCR Engines with Clustering Approach,” Egypt. Informatics J., vol. 22, no. 3, pp. 329–338, 2021, doi: 10.1016/j.eij.2020.12.004.

Y. Kobayashi, S. Mimuro, S. N. Suzuki, Y. Iijima, and A. Okada, “Basic Research on A Handwritten Note Image Recognition System that Combines Two OCRs,” Procedia Comput. Sci., vol. 192, pp. 2596–2605, 2021, doi: 10.1016/j.procs.2021.09.029.

H. Dutta and A. Gupta, “PNRank: Unsupervised Ranking of Person Name Entities from Noisy OCR Text,” Decis. Support Syst., vol. 152, no. December 2020, pp. 113662, 2022, doi: 10.1016/j.dss.2021.113662.

R. Chandramouli and P. Lee, “Infrastructure Standards for Smart ID Card Deployment,” IEEE Secur. Priv., vol. 5, no. 2, pp. 92–96, 2007, doi: 10.1109/MSP.2007.34.

R. Sushma, M. R. Devi, and N. Maheshwaram, “Automatic License Plate Recognition with YOLOv5 and Easy-OCR method,” vol. 9, no. 1, pp. 1243–1247, 2022.

W. Xinming and X. Zhibing, “The Research of Second-generation ID card’s Application in the Domestic Air Passenger Travel,” in 2012 International Conference on Medical Physics and Biomedical Engineering, 2012, vol. 33, pp. 476–482, doi: 10.1016/j.phpro.2012.05.092.

Y. Hu, T. Muhammad, B. Wu, A. Wei, X. Yang, and L. Chen, “A Simple On-Line Detection System Based on Fiber-Optic Sensing for The Realtime Monitoring of Fixed Bed Adsorption Processes of Molecularly Imprinted Polymers,” J. Chromatogr. A, vol. 1622, p. 461112, 2020, doi: 10.1016/j.chroma.2020.461112.




DOI: http://dx.doi.org/10.24042/ijecs.v2i1.13108

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