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For a full-stack web or app development, it requires a software firm or morespecifically a team of experienced developers to contribute a large portion oftheir time and resources to design the website and then convert it to code. Asa result, the efficiency of the development team is significantly reduced whenit comes to converting UI wireframes and database schemas into an actualworking system. It would save valuable resources and fasten the overallworkflow if the clients or developers can automate this process of convertingthe pre-made full-stack website design to get a partially working if not fullyworking code. In this paper, we present a novel approach of generating theskeleton code from sketched images using Deep Learning and Computer Visionapproaches. The dataset for training are first-hand sketched images of lowfidelity wireframes, database schemas and class diagrams. The approach consistsof three parts. First, the front-end or UI elements detection and extractionfrom custom-made UI wireframes. Second, individual database table creation fromschema designs and lastly, creating a class file from class diagrams.

Sketch2FullStack: Generating Skeleton Code of Full Stack Website and Application from Sketch using Deep Learning and Computer Vision