## 2 Complex-Image Layout Analysis Methods
### Layout Analysis Method Based on Machine Learning
#### 2.3.1 Layout Analysis Method Based on Support Vector Machine (SVM)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method** & **Advantage** & **Disadvantages** & **Reference** \\ \hline \multirow{4}{*}{Top-down} & Cyclic projection x-y & This algorithm has a faster & Does not work well with complex & [22] \\  & tangent algorithm & processing speed. & and skewed layouts. & \\ \cline{2-5}  & Recursive dichotomous & Optimized conventional projection & The algorithm runs inefficiently & [22] \\  & projection algorithm & methods. & and is time consuming. & \\ \hline \multirow{4}{*}{Bottom-up} & Run-Length Smoothing & Simple algorithm, stronger noise & Higher dependence on thresholds & \\  & Algorithm & immunity. & and more computationally & [25] \\  & Connected-Region & Ability to quickly detect connected & Merging rules are difficult to & \\  & Algorithm & areas in an image. & determine and must be processed & \\  & & & areas in an image. & using a large number of \\  & & & parameters. & \\ \cline{2-5}  & Voronoi diagram & Ability to have good reliability & Does not support image-area & \\  & algorithm & and accuracy in & splitting, does not work with & \\  & & electronic-document scenarios. & skewed layout images. & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & Docstrum algorithm & Ability to cope with different text & Relies on a set of threshold & \\  & & sizes and fonts. & parameters for clustering and does & \\  & & & not support image-region splitting. & \\ \hline \multirow{4}{*}{Hybrid} & \multirow{4}{*}{Texture analysis & The ability to simultaneously & \\  & & process graphics from both the & \\ \cline{1-1}  & & overall and local aspects, not only & Texture block-size division is more & \\ \cline{1-1}  & algorithms & to adapt to the more complex & difficult to determine. & \\ \cline{1-1}  & & layout of the text image, and the & \\ \cline{1-1}  & & implementation of high efficiency. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of representative algorithms in traditional layout analysis methods. segmentation based on the support vector machine. The algorithm labelled a small number of pixel points of the image as foreground samples and background samples of the blade, respectively, and built a support-vector-machine classification decision model based on the sample data. Finally, the whole-image pixels are classified according to the prediction model and the target image is segmented from the background. [PERSON] [38] proposed an improved SVM image-segmentation algorithm based on the texture and color of the image as feature vectors, mainly for complex backgrounds and color images with unclear target contours as target images to be segmented. Table 2 compares and analyses the improved image-segmentation methods based on the SVM algorithm in recent years. At this stage, the SVM algorithm is gradually broadening its scope of application, into areas such as pattern recognition and regression analysis, and other fields are used in the algorithm. From the field of pattern recognition, for the needs of image segmentation, image-edge processing, image recognition and target detection, SVM algorithms have better performance and more ideal results than traditional methods. Despite this, the SVM algorithm still has many shortcomings and is not ideal for solving multi-classification problems, and the classical SVM algorithm is only suitable for binary classification problems. The computation of the algorithm design matrix is difficult to implement for large-scale training samples due to the fact that it consumes a large amount of machine memory and runtime when the matrix order is large, and it is difficult to apply the choice of kernel functions and parameters to the segmentation of most images.