# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 5 Conclusions  
In this paper, we presented a direction-aware lightweight framework for traditional Mongolian document layout analysis that effectively addresses the unique challenges posed by vertical writing systems. The proposed approach integrates three key innovations: a modified MobileNetV3 backbone with asymmetric convolutions, a dynamic feature enhancement module with channel attention, and a direction-aware detection head with \((\sin\theta,\cos\theta)\) vector representation for accurate orientation modeling. Comprehensive experiments on the TMDLAD dataset demonstrate that our approach significantly outperforms existing methods in both accuracy and efficiency, providing a practical solution for traditional Mongolian document layout analysis that can be extended to other vertical writing systems. The modified MobileNetV3 backbone with asymmetric convolutions reduces computational complexity by 33% while better capturing the vertical patterns characteristic of traditional Mongolian text. The dynamic feature enhancement module adaptively integrates multi-scale information through channel attention, enabling effective processing of complex hierarchical structures in Mongolian documents and improving mAP by 1.6% and direction accuracy by 2.6%. The direction-aware detection head with \((\sin\theta,\cos\theta)\) vector representation explicitly models text orientation, achieving a mean absolute error of only 2.5 deg, significantly outperforming alternative direction representation methods. Our model achieves state-of-the-art performance with 0.715 mAP and 92.3% direction accuracy, outperforming both heavy ResNet-50-based models and lightweight alternatives. Compared to the best-performing ResNet-50-based model (Rol Transformer with 0.679 mAP), our approach improves detection accuracy by 3.6% while requiring only 8.3 M parameters (versus 46.8 M) and running at 28.6 FPS (versus 9.5 FPS). Even when compared to efficient lightweight models like YOLOv8n, our approach maintains superior detection accuracy (0.715 versus 0.672 mAP) while providing crucial direction prediction capability that these models lack. This balance of accuracy and efficiency makes our framework particularly suitable for real-world applications where both performance and computational resources are important considerations. The construction and release of TMDLAD, the first large-scale dataset for traditional Mongolian document layout analysis, provides a valuable resource for future research in this area. Our joint optimization approach with adaptive loss weighting effectively balances classification, localization, and direction prediction tasks, demonstrating strong performance across all layout categories with a macro-average F1 score of 0.88. The model performs consistently well across different layout categories, with particularly strong results for text and figure elements that are critical for document understanding. Future research directions include extending our approach to handle degraded historical documents, exploring semi-supervised learning techniques to reduce annotation requirements, and adapting our framework to other vertical writing systems such as traditional Chinese and Japanese. We also plan to investigate the integration of language models to enhance semantic understanding of document content. We believe our work contributes significantly to the preservation and accessibility of cultural heritage materials in traditional Mongolian script, and provides a solid foundation for document analysis systems that can effectively handle non-Latin vertical writing systems. Conceptualization, C.Z. and L.W. ; methodology, C.Z. and M.H. ; software, C.Z. ; validation, C.Z., M.H., and L.W. ; formal analysis, C.Z. ; investigation, C.Z. and M.H. ; resources, L.W. ; data curation, C.Z. and M.H. ; writing--original draft preparation, C.Z. ; writing--review and editing, L.W. ; visualization, C.Z. and M.H. ; supervision, L.W. ; project administration, L.W. ; funding acquisition, L.W.