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Background: The project work entitled as Automatic html code generation from mock-up images using Machine Learning, basically, the main objective of this project is the design cycle for a web site starts with creating mock-ups for individual web pages either by hand or using graphic design and specialized mock-up creation tools. The mock-up is then converted into structured HTML or similar markup code by software engineers. This process is usually repeated many more times until the desired template is created. In this study, our aim is to automate the code generation process from hand-drawn mock-ups. As a first step of designing of website is start to build the mock-up images for the particular web pages by operated with the hands or using mockup developer tools. It is efficiently used for the developer to transferring web pages mock-up to the coding. It’s generating the proposed system to creating the wireframe to the layout interfaces there are two techniques mostly used first is computer vision and second is deep systematic analysis. The automatic code generation is time reducing and cost effective. We have design structured an outline the design. Hand drawn mock-ups are processed using computer vision techniques and subsequently some deep learning methods are used to implement the proposed system.
Objectives: In this Automatic html code generation from mock-up images using Machine Learning, main objective is to generate HTML code from hand drawn mock-up images using Machine Learning.
Conclusions: Thus, the study Machine Learning helped to gain knowledge on both the theory and practical part. Every concept was clear and brought some interest for further learning. As stated earlier, the actual process of creation of web page takes more time and should meet the requirements, which makes developers exhausted. In order to avoid facing those circumstances, Automatic html code generation from mock-up images using Machine Learning is introduced which is great advantage.