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All Journal Widyagogik : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar Journal of ICSAR Jurnal ORTOPEDAGOGIA Jurnal Pendidikan Kebutuhan Khusus Jurnal Asesmen Dan Intervensi Anak Berkebutuhan Khusus CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia JURNAL PENDIDIKAN INDONESIA: Teori, Penelitian, dan Inovasi Jurnal Pendidikan Indonesia Gemilang Jurnal Ilmiah Pendidikan Dasar (JIPDAS) MASALIQ: Jurnal Pendidikan dan Sains Advance Sustainable Science, Engineering and Technology (ASSET) J-CEKI
Pratama, Toni Yudha
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Religion Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

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“Demata 2.0”: An On-Device AI Assistive Technology for the Visually Impaired Integrating YOLOv10 and OCR Abadi, Reza Febri; Pratama, Toni Yudha; Asmiati, Neti; Devi, Ade Anggraini Kartika; Yuwono, Joko; Dwi Setia Permana; Bahrudin, Febrian Alwan
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2380

Abstract

Accessibility to printed materials and independent recognition of the environment remain key challenges for students with visual impairments. To address this issue, this study introduces Demata 2.0, a fully offline on device multimodal AI system. The system integrates Google ML Kit for Optical Character Recognition (OCR) and the YOLOv10 model via TensorFlow Lite for object detection. A mathematical distance algorithm in the RGB color space enables color identification. Evaluation showed that object detection achieved a mean average precision of 31.83%, with an average processing speed of 2–3 FPS. For OCR, the system recorded a Character Error Rate (CER) of 4.81% and a Word Error Rate (WER) of 10.71% on printed documents. The RGB algorithm also determined the closest possible color effectively. Overall, Demata 2.0 advances assistive technology by providing an efficient and practical blueprint for AI integration.
Co-Authors Ade Anggraini Kartika Devi Amelia, Dwi Asmiati, Neti Asshifa, Zahra Rahma Ataqi, Andre Eprilia Aulia, Ishna Nahwal Az-zahra, Afifah Sarah Bahrudin, Febrian Alwan Dwi Setia Permana Harits, Achmadi Fajar Hartanti, Herma Raidha Imas Diana Aprilia Lutfianti, Zerlinda Maharani, Ferina Agnis Maslahah, Sayidatul Meillinia, Aisha Mulia, Dedi Mustika, Rina Anggiani Nurhayati, Yayah Nursabrina, Putri Mayunda Putri, Khalda Andhika Rahmawati, Euis Rasnah, Karlina Dieka Reza Febri Abadi Ristanti, Ristanti Salsabiil, Dhea Salmaa Sidik, Sistriadini Alamsyah Siti Musayaroh Utami, Yuni Tanjung Yani, Auliya Fitri Yuwono, Joko