pic2code: Generating HTML Code from Handwritten Picture

The recent development of automated code generation is an expanding area of investigation within the realm of research. In the realm of User Interface code design, a considerable amount of attention has conventionally been devoted to the meticulous creation of visually appealing designs. This research presents an innovative technique that has been developed to effortlessly convert hand-drawn sketches into hypertext markup language code. This breakthrough enables anyone with limited experience to easily create HTML websites using basic diagrams. The methodology used in this study is based on the highly acclaimed YOLOv3 model, which is widely recognized for its exceptional object detection capabilities. In the methodology, a deep convolutional neural network algorithm has been carefully integrated into the Sketch2Code data set. This procedure guarantees that our model not only attains notable efficacy but also regularly produces precise outcomes. Through extensive evaluations, the accuracy rate of our suggested system is found to be 87.71%, as measured by Mean Average Precision. In the current period characterized by the significance of online development, the ability to automatically convert designs into HTML code streamlines the process of constructing web interfaces, while enabling designers and developers to explore novel opportunities.