# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 4 Experiments
### Comparison with State-of-the-Art Methods  
Table 2 compares our method with state-of-the-art object detection methods on the TMDLAD dataset, including both heavy ResNet-50-based models and lightweight models. Our approach significantly outperforms all previous methods in detection accuracy (mAP).  
Among the ResNet-50-based models, our approach achieves a 3.6% improvement in mAP and a 12.8% improvement in direction accuracy compared to the best-performing baseline (Rol Transformer). This demonstrates the effectiveness of our direction-aware design for traditional Mongolian document layout analysis.  
When compared with lightweight models such as MobileNetV3-SSD, EfficientDet-Lite0, YOLOv5s, YOLOv8n, and PP-PicoDet-S, our approach still maintains superior detection accuracy while offering competitive efficiency. Although these lightweight models provide faster inference speeds (35-48 FPS) and some have fewer parameters (3.2-7.2 M), they achieve significantly lower detection accuracy (0.623-0.672 mAP) and lack direction prediction capability, which is crucial for traditional Mongolian document analysis. Among these lightweight models, YOLOv8n achieves the best performance with an mAP of 0.672, but still falls short by 4.3% compared to our approach.  
The superior performance of our model can be attributed to three key factors: (1) the modified MobileNetV3 backbone with asymmetric convolutions that better capture vertical text patterns, (2) the dynamic feature enhancement module that adaptively fuses multi-scale features, and (3) the direction-aware detection head that explicitly models text orientation using the \((\sin\theta,\cos\theta)\) vector representation.  
Our model strikes an optimal balance between accuracy and efficiency, achieving the highest mAP (0.715) while maintaining real-time performance (28.6 FPS) with a modest parameter count (8.3 M). This makes it particularly suitable for traditional Mongolian document layout analysis, where both accuracy and direction awareness are critical. Even  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Backbone** & **AP\({}_{\text{50}}\uparrow\)** & **mAP \(\uparrow\)** & **DA \(\uparrow\)** & **Params \(\downarrow\)** & **FPS \(\uparrow\)** \\ \hline Faster R-CNN & ResNet-50 & 0.762 & 0.591 & - & 41.5 M & 12.5 \\ Mask R-CNN & ResNet-50 & 0.778 & 0.613 & - & 44.2 M & 9.8 \\ Cascade R-CNN & ResNet-50 & 0.795 & 0.638 & - & 69.2 M & 8.7 \\ FCOS & ResNet-50 & 0.783 & 0.624 & - & 32.1 M & 14.3 \\ Oriented R-CNN & ResNet-50 & 0.821 & 0.667 & 0.782 & 43.7 M & 10.2 \\ RoI Transformer & ResNet-50 & 0.834 & 0.679 & 0.795 & 46.8 M & 9.5 \\ MobileNetV3-SSD & MobileNetV3 & 0.723 & 0.623 & - & 4.3 M & 35.2 \\ EfficientDet-Lite0 & EfficientNet-B0 & 0.742 & 0.645 & - & 3.2 M & 30.5 \\ YOLOv5s & CSPNet & 0.756 & 0.658 & - & 7.2 M & 45.7 \\ YOLOv8n & CSPNet & 0.768 & 0.672 & - & 3.2 M & 48.3 \\ PP-PicoDet-S & ESNet & 0.749 & 0.651 & - & 3.5 M & 42.1 \\ \hline Ours & Modified MobileNetV3 & **0.867** & **0.715** & **0.923** & 8.3 M & 28.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art methods on the TMDLAD dataset.  
the best-performing lightweight model (YOLOv8n with an mAP of 0.672) falls short by 4.3% in detection accuracy and lacks direction prediction capability entirely.  
The experimental results demonstrate that our approach effectively addresses the unique challenges of traditional Mongolian document layout analysis, providing a practical solution that balances accuracy, direction awareness, and computational efficiency.