# Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
## 1 Introduction  
At this stage, artificial intelligence has developed into the intersection of many disciplines such as cognitive science, psychology, art, and computer science [1, 2]. With the development of deep learning and neural networks, more and more researchers combinethem with design, such as intelligent image color matching and intelligent image quality evaluation [3;4]. And the complex layout images obtained from text, graphics, images, and other layout elements through the process of artistic design, pre-press processing, typesetting and so on are widely used in packaging, posters, book binding, and so on [5;6]. At present, the study of complex layout images is only for simple document images, and there are few reports on the design of complex layout images. Therefore, there is an urgent need for more scientific, intelligent, and highly accurate methods and techniques for analyzing the layout of complex layout images. Compared with such complex images as posters, document images, newspapers, and other complex images have a single background and neatly arranged text, graphics, tables and other foregrounds. Currently, most domestic and international research on complex layout image analysis focuses on document images, newspapers, and similar structured materials, while relatively few studies have addressed layout analysis in traditional poster designs, brochures, and other visual media. Traditional layout analysis can be roughly categorized into three approaches: top-down approach [7]; bottom-up approach [8]; hybrid approach [9]. Amidst the rise of neural networks, a number of researchers have combined layout analysis with computers, deep learning, neural networks, and other techniques to explore complex layout image analysis [10]. [PERSON] et al. [11] proposed a method for document image layout analysis with explicit edge embedding networks, which exploits the model to overcome the data scarcity by using an integrated document approach compared to the traditional methods. [PERSON] et al. [12] proposed a design space to describe the design elements in advertising posters and introduced a design sequence to rationalize the design decisions of human designers in creating posters. There is still much room for research in the field of combining visual communication design images with deep learning techniques. The central question of this research is how to perform automatic layout design through artificial intelligence. The first step towards enabling automatic layout design with AI is to parse and recognize the constituent components of the layout. As suggested in previous work [13], this study adopts deep learning techniques to accurately locate and classify layout elements in complex visual designs such as movie posters. The presence of different foregrounds and complex graphical backgrounds in natural images like movie posters and the text in the images with various, colors, sizes, orientations, and textures make it even more challenging to perform an image layout analysis for complex layout images like movie posters. Aiming at the problem of difficulty in analyzing the layout of visual communication images, this paper takes movie posters as the research object and carries out the construction of movie poster dataset. Based on the improved DeepLabv3+ network model, the segmentation and recognition of text, theme and graphic regions of movie posters were performed. Finally, according to the layout segmentation results, the positional relationship of the theme area, text area and graphic area is analyzed, and the layout analysis of the movie poster is performed using the GoogLeNet image classification network model. In summary, this study proposes a two-stage approach for analyzing complex movie poster layouts. An improved DeepLabv3+ model with a lightweight backbone and optimized ASPP is used for accurate element segmentation, and a GoogLeNet-based classifier is employed to determine layout types. A high-resolution annotated dataset of 2300 posters is also constructed to support the experiments.