• Prajakta Rajesh Nimbalkar
• Vina M. Lomte
• Rushikesh Kalbande
• Bhagyashri Ukande
• Snehal Kande

Image inpainting technology plays an important role in the process of digitalizing ancient literature. It helps to recover the partially missing or stained characters and has shown remarkable success in the field of image inpainting. In this paper, we propose a new CNN model using the idea of edge recovery and optimize this model with spatial attenuation mask and conditional labelling to improve performance. Image inpainting is nothing but the art of repairing the old and deteriorated image, has been around for many years, and now it has gained popularity due to various latest technologies in the image processing. To development of this model, Image inpainting is necessary to repair the damaged regions or to remove unnecessary objects to improve image quality for other post-processing tasks To evaluate image inpainting quality is the initial goal of the proposed method.

Feature extraction, Convolutional Neural Network, Generative Adversarial Network, EdgeConnect

"Image Inpainting Approach with Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.o579-o586, May-2023, Available :http://www.jetir.org/papers/JETIR2305F77.pdf

"Image Inpainting Approach with Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppo579-o586, May-2023, Available at : http://www.jetir.org/papers/JETIR2305F77.pdf