Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Engineering, Technology and Natural Sciences (IJETS)
Utami, Fitri Catur
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Developing an Optical Character Recognition Application for Mobile-Based Recognition and Translation of Arabic-Malay Characters Utami, Fitri Catur; Putra, Fajri Profesio
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 7 No 1 (2025): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v7i1.437

Abstract

This research develops an Optical Character Recognition (OCR) application to recognize and translate Arabic-Malay script, widely used in Bengkalis and surrounding regions. The application is designed to facilitate learning and interaction with the script through features for text recognition and translation. To enhance OCR accuracy, advanced image preprocessing techniques and a comprehensive training dataset were employed. Testing under various lighting conditions showed an average recognition accuracy of 25%. Performance was optimal under well-lit conditions, achieving 100% accuracy at 1639 lux, but accuracy significantly declined in low-light environments, particularly at 3.5 lux, where it dropped to 0%. While this result demonstrated the system’s potential, a comparison with similar OCR systems indicates significant room for improvement, particularly in recognizing complex or handwritten Arabic-Malay characters. The results highlight challenges related to lighting conditions, including variations in light sources and angles, which greatly affect recognition accuracy. Indeed, future work should focus on expanding the training dataset with diverse samples, refining preprocessing techniques such as automatic brightness adjustment, and implementing advanced machine learning models like Convolutional Neural Networks (CNNs). This application holds significant potential for practical uses in education, translation services, and local communication, contributing to preserving and promoting the Arabic-Malay script as part of cultural heritage in the modern era.
Co-Authors Putra, Fajri Profesio