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All Journal Journal of System and Computer Engineering Buletin Poltanesa
Kautsar Hasby Dastien Fredila
Gunadarma University
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education

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Developing a Manga Translation Application Using YOLOv8 and Automatic Text Detection Kautsar Hasby Dastien Fredila; M Ridwan Dwi Septian; Agustine Hana Masitoh; Intan Meutia Sari
Poltanesa Vol 26 No 2 (2025): December 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i2.3516

Abstract

Japanese manga has evolved into a worldwide cultural phenomenon. Despite its popularity, many official editions of manga remain in raw format, namely untranslated Japanese versions. This creates difficulties for non-Japanese especially Indonesian readers in understanding the storyline. The swift progress of Artificial Intelligence and Machine Learning provides innovative solutions to overcome this barrier. One promising approach is the use of Optical Character Recognition technology to detect textual elements in manga images and translate them automatically with the help of object detection models such as You Only Look Once version 8. Building on this approach, the present study develops a web-based manga translation application by applying the Software Development Life Cycle method, which includes requirement analysis, design, implementation, testing, and maintenance. The system integrates You Only Look Once version 8 for panel detection, Manga Optical Character Recognition for Japanese text recognition, and translation libraries for automatic text replacement. The novelty of this research lies in offering an end-to-end translation solution that supports both image and portable document format inputs, while also providing downloadable outputs for improved usability. In this way, the study contributes to the fields of Optical Character Recognition and machine translation, while delivering a functional tool that enables Indonesian readers to access Japanese manga without language barriers.
Perbandingan Kinerja YOLOv8 dan YOLOv11 untuk Deteksi Area Teks Manga Berdasarkan Metrik Intersection over Union M Ridwan Dwi Septian; Kautsar Hasby Dastien Fredila; Ericks Rachmat Swedia; Margi Cahyanti
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2712

Abstract

Manga has complex visual characteristics, such as variations in speech bubble shapes, diverse text orientations, and dense background illustrations, which complicate the automatic text area detection process. Detection errors in the form of false positives and false negatives can cause text areas to be localized inaccurately and affect the processing at subsequent stages. This study compares the performance of YOLOv8m and YOLOv11m in detecting text areas in Japanese manga using the Intersection over Union (IoU) metric. The dataset consists of 551 manga images annotated into three classes, namely clean_text, messy_text, and text_bubble. Both models were trained under the same parameter configuration for 60 epochs to ensure an objective comparison. The evaluation was performed on 50 test images covering 564 text objects. The test results show that YOLOv11m obtained an average IoU of 0.7598, which is higher than YOLOv8m (0.7196). In addition, YOLOv11m exhibited a faster inference time of 1158.72ms compared with 1276.23ms for YOLOv8m. Based on these results, YOLOv11m demonstrated superior performance over YOLOv8m in terms of both localization accuracy and computational efficiency for the Japanese manga text area detection task.
Co-Authors Agustine Hana Masitoh Ericks Rachmat Swedia Intan Meutia Sari M Ridwan Dwi Septian M Ridwan Dwi Septian Margi Cahyanti