# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 1 Introduction  
Document layout analysis plays a crucial role in digitizing and preserving historical documents. However, traditional Mongolian documents present unique challenges due to their distinctive writing system and complex structural arrangements. Unlike conventional horizontal text layouts, traditional Mongolian texts flow vertically from top to bottom,with lines progressing from left to right. This unique writing system, combined with diverse layout patterns such as multi-column arrangements, mixed text orientations, and hierarchical structures, creates significant challenges for existing document layout analysis methods. Furthermore, the scarcity of publicly available datasets and efficient analysis tools has impeded the development of automated solutions for processing traditional Mongolian documents, thereby limiting their accessibility and digital preservation. Recent advances in deep learning have demonstrated promising results in document layout analysis [1]. Existing approaches can be broadly categorized into two types. Segmentation-based approaches [2; 3; 4] attempt to classify individual pixels into different layout categories but frequently struggle with densely arranged vertical text regions characteristic of traditional Mongolian documents. Detection-based methods, including Faster R-CNN [5], RetinaNet [6], and Mask R-CNN [7], focus on generating bounding boxes for layout elements but exhibit sensitivity to hyperparameter settings and often fail to fully capture the unique spatial relationships in vertical text arrangements. Although recent work has explored continual learning techniques to address model adaptation challenges [8], significant limitations remain in handling the vertical nature of Mongolian text. Recent efforts have focused on developing more efficient architectures for document layout analysis. YOLO-based models [9; 10; 11] have demonstrated encouraging results through their efficient single-stage detection architecture. However, these models continue to encounter difficulties in effectively handling the vertical nature and intricate structural patterns inherent in traditional Mongolian documents. Similarly, lightweight models [12; 13] offer compelling efficiency advantages and have shown promise in document layout analysis with limited data. Nevertheless, these models frequently struggle to address three primary challenges: (1) accurate handling of vertical text regions, (2) preservation of spatial relationships between layout elements, and (3) achievement of computational efficiency with limited resources. Moreover, direction-aware feature representation, which is essential for accurate analysis of vertical text layouts, remains largely unexplored in existing lightweight frameworks. To address these limitations, we propose a direction-aware lightweight framework for traditional Mongolian document layout analysis that offers several significant innovations to existing approaches. Our work makes the following key contributions:  
* Direction-optimized feature extraction: We introduce a novel modified MobileNetV3 backbone with asymmetric convolutions specifically designed for vertical text analysis. This innovation reduces computational complexity by 33% while simultaneously enhancing the model's ability to capture the distinctive vertical characteristics of traditional Mongolian text. Unlike conventional approaches that use symmetric kernels, our asymmetric design (3 \(\times\) 1 followed by 1 \(\times\) 3) creates an efficient receptive field pattern that aligns with the vertical flow of traditional Mongolian text. * Adaptive information fusion: We develop a dynamic feature enhancement module with channel attention that intelligently integrates multi-scale information. This module adaptively adjusts feature importance based on input characteristics, enabling the model to effectively process the complex hierarchical structures found in traditional Mongolian documents. Our experiments demonstrate that this approach improves mAP by 1.6% and direction accuracy by 2.6%.