## 2 Complex-Image Layout Analysis Methods
### Layout Analysis Method Based on Machine Learning
#### 2.3.2 Layout Analysis Based on Neural Networks  
Before the rise of neural networks, traditional image-segmentation methods used some low-level semantic information to segment images, which ran fast, had low complexity, and could maintain good edge information. However, in real life, some objects have complex structures with great internal variability, so traditional image-segmentation algorithms can misjudge the background and the target, creating a lack of segmentation accuracy. To solve these problems, researchers have used neural networks to classify all pixels in segmented images to obtain semantic segmentation results. Based on some classical convolutional classification networks (such as AlexNet, VGGNet, GoogLeNet, ResNet, etc. ), [PERSON] [39] proposed a framework for image semantic segmentation, Fully Convolutional Networks (FCN). The main idea is to replace the fully \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Pub. Year** & **Advantage** & **Disadvantages** \\ \hline [PERSON] et al. [34] & 2019 & SVM combined with mean clustering algorithm for automatic acquisition of training samples. & Not applicable to segmentation of complex layout images. \\ \hline [PERSON] et al. [35] & 2019 & Human observation of the color characteristics of the target and background areas, manual selection of sample points by hand, which is time-consuming and labor-intensive. \\ \hline [PERSON] et al. [36] & 2020 & Combining image phase consistency and texture features to form new feature vectors for layout segmentation. & The accuracy of determining the boundaries between graphs and images is low due to poor graph regularity and high ambiguity. \\ \hline [PERSON] et al. [37] & 2022 & Classification of image pixel points by labelling foreground and background samples in the image. & Requires manual selection of sample points by hand, which is time-consuming and labor-intensive. \\ \hline [PERSON] et al. [38] & 2022 & The SVM algorithm was improved by adding the hue–saturation-intensity (HIS) color space channel and selecting the RGB and HIS dual color space channels as feature vectors to classify the pixels. & The selected kernel functions and parameters are only applicable to a small number of image segmentations \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of SVM-based layout analysis methods. connected layer of classical neural networks with a convolutional layer, which can adapt to input- and output-classification images of any size. Since the FCN network model was proposed, image semantic segmentation is beginning to enter the research period of image pixel segmentation. [PERSON] [40] proposed a complex document image-segmentation method based on a label pyramid network and deep watershed transform, which can segment document images into instance-sensing regions, where the backbone of the tag pyramid network is the FCN network used. The principle of segmentation is to add the output image of multiple tasks of LPN into a probability graph and perform the watershed transformation on it to segment the document image into the instance-perception region. [PERSON] [41] proposed a Pyramid Scene Parsing Network (PSPNet) based on the idea of a spatial pyramid pooling module, which provided a superior framework for pixel-level prediction and achieved good performance in scene-segmentation tasks. [PERSON] [42] proposed a multi-focus image-segmentation fusion method based on PSPNet to solve the problem whereby traditional multi-focus image fusion methods could not make full use of spatial-context information. The method used PSPNet to extract the focus region of the source image, and ConvCRF to optimize the region and carry out multi-focus image fusion. The Google team proposed a series of semantic segmentation algorithms the DeepLab series. DeepLabv1 was proposed in 2014 [43] and achieved second place in the segmentation task on the PASCAL VOC2012 dataset; 2017 to 2018 saw the successive proposals of DeepLabv2, DeepLabv3, and DeepLabv3+.