Alam, Md. AshrafulMahbub, Mohammed Julfikar AliRahmatullah, S. AfsanRahman, G M SohanurZillanee, Abu HasnayenAkib, Aknur Kamal2022-06-012022-06-0120222022-01ID 21341031ID 21241075ID 21341049ID 18301159ID 18301209http://hdl.handle.net/10361/16818Cataloged from PDF version of thesis.Includes bibliographical references (pages 23-26).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.Processing sketches to produce realistic images is an intriguing idea in the world of emerging Artificial Intelligence. We present a Generative Adversarial Network (GAN) based methodology that creates satisfactory images for the most prevalent categories in our approach. The proposed approach is applicable not just to people, but also to animals, objects and foods. The system takes a sketch and analyzes it using a powerful neural engine to produce new photographs that resemble realistic images. We also used a data augmentation method to dramatically increase the variety of data available for training models. The proposed model has achieved approximately 96.36% accuracy over generating sketch to realistic images of people and 40.63% accuracy for objects and animals. Moreover, about 76.63% accuracy on generating sketches from strokes on an average from people class.26 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.GANSketch to realistic imageData augmentationDiversity of domainCognitive learning theory (Deep learning)Artificial intelligenceThesis