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

Rodzan Iskandar, M. Kesuma
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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.
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基于实时光学字符识别的身份证检测系统设计
本课题旨在设计一种基于光学字符识别(OCR)的实时身份证检测系统。OCR通过摄像头检测并将信息记录到CSV文件中。希望它能成为印尼的行政解决方案之一,利用现有身份证实时使用OCR。该研究方法于2021年8月以身份证为对象独立开展。所涉及的工具是惠普英特尔酷睿i5第7代笔记本电脑上的320x320像素网络摄像头。Easy OCR使用的软件是基于pytorch的。以SSD MobileNet V2 FPNLite 320x320作为TensorFlow的预训练模型,使用TensorFlow对象检测算法检测ID卡。研究人员使用网络摄像头在不同的光线条件和方向下收集身份证图像,并使用标签对其进行标记。研究人员只用20张照片训练它。经过3000步的训练,研究人员得到了0.17的损失和0.95的损失。因此,使用OCR的身份证检测工具运行良好。
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