# Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
## 6 Conclusions  
Based on the analysis and generalization of the characteristics of complex layout images, this paper proposes a complex image layout analysis method based on improved DeepLabv3+ model. By improving the backbone network and the ASPP module in the DeepLabv3+ model, the network is easier to train and the image pixel recognition accuracy is higher, which enhances the model's ability to segment smaller categories of targets; secondly, based on the summarization of the poster image layout categories in this paper, we combine the GoogLeNet network model with the layout classification and analysis of the complex image. Experimental results show that the improved method proposed in this study achieves segmentation accuracy comparable to that of classical models. However, it significantly enhances the model's ability to segment smaller target categories and reduces training time while maintaining overall accuracy. Despite these advancements, the model still struggles to accurately segment text with irregular orientations--such as tilted or curved fonts--due to the complex design styles often present in poster layouts. This remains a key challenge to be addressed in future work.  
In particular, future research may consider integrating recent consensus-based segmentation strategies, such as kinetic modeling frameworks [28, 29], which have shown strong potential in improving robustness and handling layout uncertainty. These model-driven approaches can complement current deep learning methods and further enhance the system's generalization ability across diverse design structures.  
Moreover, our experiments confirm that arbitrarily oriented text--such as slanted, rotated, and curved styles--often disrupts the alignment between text strokes and convolutional filters, leading to blurred boundaries and fragmented predictions. To address this issue, recent advances such as the Hi-SAM framework proposed by [PERSON] et al. [30] have demonstrated promising capabilities by adapting the Segment Anything Model (SAM) for the hierarchical segmentation of complex text layouts with minimal supervision.  
Additionally, the DASM module proposed by [PERSON] et al. [31], which integrates dual spatial-channel attention into the decoder stage, has proven effective in enhancing text boundary localization under challenging lighting and distortion conditions. Building on these insights, future work will explore combining kinetic segmentation models with orientation-aware decoding, dual-attention mechanisms, and prompt-guided fine-tuning to improve robustness against spatial transformations and enhance adaptability in complex design scenarios.  
Conceptualization, Y.Z.; methodology, Y.Z. and Y.P.; software, Y.P.; validation, X.L., W.Z. and Y.C.; resources, H.H. and L.Z.; writing--original draft preparation, Y.P.; writing--review and editing, Y.Z. and D.L.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province, Grant No. 2021 JJ30218.  
Not applicable.  
The datasets and source code used in this study are currently not publicly available due to copyright restrictions and ongoing research work. However, related materials can be provided upon reasonable request to the corresponding author.  
The authors declare no conflicts of interest.