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Today, more and more, it is necessary that most applications and documentsdeveloped in previous or current technologies to be accessible online oncloud-based infrastructures. That is why the migration of legacy systemsincluding their hosts of documents to new technologies and onlineinfrastructures, using modern Artificial Intelligence techniques, is absolutelynecessary. With the advancement of Artificial Intelligence and Deep Learningwith its multitude of applications, a new area of research is emerging - thatof automated systems development and maintenance. The underlying work objectivethat led to this paper aims to research and develop truly intelligent systemsable to analyze user interfaces from various sources and generate real andusable inferences ranging from architecture analysis to actual code generation.One key element of such systems is that of artificial scene detection andanalysis based on deep learning computer vision systems. Computer vision modelsand particularly deep directed acyclic graphs based on convolutional modulesare generally constructed and trained based on natural images datasets. Due tothis fact, the models will develop during the training process natural imagefeature detectors apart from the base graph modules that will learn basicprimitive features. In the current paper, we will present the base principlesof a deep neural pipeline for computer vision applied to artificial scenes(scenes generated by user interfaces or similar). Finally, we will present theconclusions based on experimental development and benchmarking againststate-of-the-art transfer-learning implemented deep vision models.

CloudifierNet -- Deep Vision Models for Artificial Image Processing