## 2 Complex-Image Layout Analysis Methods
### Traditional Layout Analysis Methods  
Traditional layout analysis can be broadly classified into three methods: top-down methods [19], bottom-up methods [20], and hybrid methods [21]. With the continuous development of machine learning and neural network technology, emerging analysis methods have appeared in the field of layout analysis, mainly based on machine learning. The top-down approach is essentially a decomposition process, mainly starting from the page as a whole and recursively segmenting the image, based on global information. Its advantages include simplicity of operation, efficiency, intuitiveness, and better results when the layout features are more intuitive. A drawback to this method is that it is not suitable for analysis of a complex layout image. The most classic of these methods is the projection-based approach [22], which achieves effective segmentation by studying the image projection histogram. Examples include circular projection x-y tangent methods and recursive dichotomous projection methods. For the circular projection x-y tangent method, the image is first projected and the best segmentation point is determined by the x, y direction, so that the image becomes two parts, and then the segmentation continues, cycling through the process until no segmentation position is possible. This method is fast but does not work well when dealing with more complex and skewed layouts. For the recursive dichotomous projection method, the traditional projection method is optimized from the analysis of the projection results to solve the problems associated with through-field projection. Its main idea is based on the projection histogram peak-and-trough distribution state, determining the best original image segmentation. If the scanned page has no through-column and no intuitive peaks and valleys on the projection, the region can be folded in half for projection. When it still cannot be analyzed, the projection area needs to be continued to be reduced until the peak and valley features are generated, and  
Figure 1: Example of document layout analysis [18]. Figure 2: Example of poster layout analysis. some of the same attribute blocks are subsequently merged, according to the corresponding rules [23]. The bottom-up layout analysis method is based on the idea of image processing, starting from finding the connectivity domains of layout images. Firstly, we obtain all the layout information at the bottom, then merge the connected regions with the same attributes in order to obtain the layout segmentation results, and then identify each segmentation region according to specific features. Its advantages include more comprehensive layout information, suitable for complex layout structures. The disadvantage is the long processing time and low efficiency. The representative algorithms include the connected-region algorithm [24], run-length smoothing algorithm [25], Voronoi diagram algorithm [26], etc. The main idea of the connected domain-based segmentation method is to first find all the connected domains in the image, and then merge them into a larger connected domain based on the intra- and inter-character and line spacing of the text [27; 28]. [PERSON] [29] improved the traditional method of connected domains by first expanding the region of individual fonts in the image, then fuzzy integrating the image using the connected spacing statistics, and finally performing the connected-region segmentation of the image. The run-length smoothing algorithm (RLSA) [25] is a pre-processing method for layout segmentation that draws on run-length encoding, after which the processed layout image is divided into isolated sub-regions, and the effective classification and merging of these regions is the key to layout segmentation. [PERSON] [30] proposed a research method based on the adaptive run-length smoothing algorithm.