Mochammad Bustanul Ilmi
Fakultas Ilmu Komputer, Universitas Brawijaya

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Sistem Deteksi Dini Rambu Petunjuk Arah Otomatis berdasarkan Optical Character Recognition (OCR) berbasis Raspberry Pi Mochammad Bustanul Ilmi; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The car is a means of daily transportation which has become a primary need in carrying out daily activities. Not only is a public car used by someone for transportation, but many people use private cars for their transportation needs. In today's private car many security measures have been applied, such as parking sensors, airbags, dash cameras, parking cameras, door lock alarms, etc. The research will discuss the dash camera which in this study will improve the function of the dash camera. In this research, we will upgrade the dash camera so that it can also function as a direction sign detector, which usually the driver must lift his head to see the sign. But this system will help the driver see the sign just by looking at the LCD monitor in the car or just by listening to the sound that will be issued by the car sound. The system uses shape detection, optical character recognition (OCR), and text to speech which are collaborated to detect these signs which will then be displayed on the LCD monitor of the car and will output sound to indicate where it has to take the direction of the intended path. From the test results, almost all sample images can detect the direction signs, but if the signs are in poor lighting, the image crop position is not symmetrical and not straight, then the sign will not be successfully read and processed by the system. And according to the results of the test, the resulting system results get the percentage of success for the detection of arrow directions of 87.5% success and 12.5% ​​fail or error. Then for the accuracy value of the detection results using optical character recognition (OCR) get a percentage of 57.45% as the average accuracy value of the 15 detected destination directions.