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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Aiti: Jurnal Teknologi Informasi Infotek : Jurnal Informatika dan Teknologi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Bulletin of Computer Science Research Journal of Information Technology (JIfoTech) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science SmartComp SAINTEK Infact: Jurnal Sains dan Komputer Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) UPGRADE: Jurnal Pendidikan Teknologi Informasi
Agustinus Rudatyo Himamunanto
Universitas Kristen Immanuel
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Sistem Pengenalan Citra Dokumen Teks Terdistorsi menjadi Teks Menggunakan Metode Deep Learning Talenta Teholi Zalukhu; Agustinus Rudatyo Himamunanto; Haeni Budiati
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 1 (2026): JANUARY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i1.4700

Abstract

A common issue in document image processing is the inability of OCR systems to accurately read text from blurred images. This study aims to develop a deep learning-based OCR pipeline capable of recognizing text in blurred document images. The process begins with image enhancement using the DnCNN model for deblurring, followed by character segmentation and classification of A–Z characters using a CNN trained on the EMNIST Letters dataset. The recognized characters are then reconstructed into complete text. Experiments were conducted on 300 blurred images with varying levels of blur (low, medium, and high). Evaluation using PSNR and SSIM metrics showed improvements in image quality, with an average PSNR of 29,56 dB and SSIM of 0.89. Furthermore, the character classification accuracy reached 95.64%. Compared to the baseline (direct Tesseract OCR without deblurring), the proposed system showed a significant improvement in text readability. These results demonstrate the effectiveness of CNN-based approaches in enhancing OCR performance on blurred document images.
Co-Authors Ama, Marniati Triningsi Tamo Bolo, Naomi Tena Budet, Vincensa Woytimena Chrisdyanto, Daniel Nourman Dangga, Nofrilia Debora, Agnes Monica Deona Carol Gerjana Rijke Saijuna Desri A.F. Waruwu Erwin Oktaf Kurniawan Waruwu Febe Maedjaja Gea, Juli Hartati Gogor Christmass Setyawan Gulo, Bintang Karmila Haeni Budiati Haeny Budiati Jatmika Kristian Juri Damai Lase Listra Frigia Missianes Horhoruw Magdalena, Mensiana Nelvina Adonia Niasiska, Niasiska Novela, Depni Nurcahyo, Azriel Christian Olam, Enos Nikodemus Samuel Tora Setyawan, Gogor Setyawan, Gogor C. Setyawan, Gogor Chrismass Sumihar, Yo’el Piter Sumihar, Yo’el Pieter Supriadi, Joseph Susilo, Zefanya Damar Aristo Swasono, Henoch Yanuar Ari Swasono, Natan Enggal Talenta Teholi Zalukhu Tasane, Elshaddai L Wanda, Melifan Widyadinata, Irvan Zalukhu, Jean Pinter Son