Efficient Image Inpainting for Handwritten Text Removal Using CycleGAN Framework

Somanka Maiti, Shabari Nath Panuganti, Gaurav Bhatnagar () and Jonathan Wu
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Somanka Maiti: Department of Mathematics, Indian Institute of Technology Jodhpur, Karwar, Jodhpur 342030, Rajasthan, India
Shabari Nath Panuganti: Department of Electrical Engineering, Indian Institute of Technology Jodhpur, Karwar, Jodhpur 342030, Rajasthan, India
Gaurav Bhatnagar: Department of Mathematics, Indian Institute of Technology Jodhpur, Karwar, Jodhpur 342030, Rajasthan, India
Jonathan Wu: Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Ave, Windsor, ON N9B 3P4, Canada

Mathematics, 2025, vol. 13, issue 1, 1-17

Abstract: With the recent rise in the development of deep learning techniques, image inpainting—the process of restoring missing or corrupted regions in images—has witnessed significant advancements. Although state-of-the-art models are effective, they often fail to inpaint complex missing areas, especially when handwritten occlusions are present in the image. To address this issue, an image inpainting model based on a residual CycleGAN is proposed. The generator takes as input the image occluded by handwritten missing patches and generates a restored image, which the discriminator then compares with the original ground truth image to determine whether it is real or fake. An adversarial trade-off between the generator and discriminator motivates the model to improve its training and produce a superior reconstructed image. Extensive experiments and analyses confirm that the proposed method generates inpainted images with superior visual quality and outperforms state-of-the-art deep learning approaches.

Keywords: image inpainting; handwritten text; deep learning; CycleGAN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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