Image Restoration: A LSHADE-GAN Approach for Yoruba Documents

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dc.contributor.authorLawal, Olamilekan Lawal
dc.contributor.authorAjao, Jumoke Falilat
dc.contributor.authorIsiaka, Mope Rafiu
dc.date.accessioned2026-05-16T19:58:39Z
dc.date.available2026-05-16T19:58:39Z
dc.date.issued2024-04-22
dc.description.abstractThe paper introduces an innovative approach to reconstructing historical handwritten texts, specifically focusing on Yoruba documents. This method combines a generative adversarial network (GAN) with the LSHADE algorithm to create the LSHADE-GAN model. Trained on a curated dataset of five degraded Yoruba documents, this model surpasses traditional image-processing techniques and deep learning-based methods in performance. Evaluation of the LSHADE-GAN model using two samples reveals F-measures of 61.83% and 78.02%, showcasing its superiority over DE-GAN (58.24% and 75.23%) and PSO-GAN (51.67% and 66.46%) approaches. Additionally, the model demonstrates enhanced PSNR and visual quality, underscoring its effectiveness in preserving cultural heritage through accurate reconstruction of historical texts.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7279
dc.language.isoen
dc.publisherProceedings of International Computing and Communication Conference (13C 2024)
dc.titleImage Restoration: A LSHADE-GAN Approach for Yoruba Documents
dc.typeOther
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