Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
Traditional Mongolian document layout analysis faces unique challenges due to its vertical writing system and complex structural arrangements. Existing methods often struggle with the directional nature of traditional Mongolian text and require substantial computational resources. In this paper, we...
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MDPI AG
2025-04-01
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| author | Chenyang Zhou Monghjaya Ha Licheng Wu |
| author_facet | Chenyang Zhou Monghjaya Ha Licheng Wu |
| author_sort | Chenyang Zhou |
| collection | DOAJ |
| description | Traditional Mongolian document layout analysis faces unique challenges due to its vertical writing system and complex structural arrangements. Existing methods often struggle with the directional nature of traditional Mongolian text and require substantial computational resources. In this paper, we propose a direction-aware lightweight framework that effectively addresses these challenges. Our framework introduces three key innovations: a modified MobileNetV3 backbone with asymmetric convolutions for efficient vertical feature extraction, a dynamic feature enhancement module with channel attention for adaptive multi-scale information fusion, and a direction-aware detection head with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mo form="prefix">sin</mo><mi>θ</mi><mo>,</mo><mo form="prefix">cos</mo><mi>θ</mi><mo>)</mo></mrow></semantics></math></inline-formula> vector representation for accurate orientation modeling. We evaluate our method on TMDLAD, a newly constructed traditional Mongolian document layout analysis dataset, comparing it with both heavy ResNet-50-based models and lightweight alternatives. The experimental results demonstrate that our approach achieves state-of-the-art performance, with 0.715 mAP and 92.3% direction accuracy with a mean absolute error of only 2.5°, while maintaining high efficiency at 28.6 FPS using only 8.3 M parameters. Our model outperforms the best ResNet-50-based model by 3.6% in mAP and the best lightweight model by 4.3% in mAP, while uniquely providing direction prediction capability that other lightweight models lack. The proposed framework 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
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| publishDate | 2025-04-01 |
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| spelling | doaj-art-ce403047326949fc902365909bc9a0ff2025-08-20T02:28:16ZengMDPI AGApplied Sciences2076-34172025-04-01158459410.3390/app15084594Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout AnalysisChenyang Zhou0Monghjaya Ha1Licheng Wu2School of Chinese Ethnic Minority Languages and Literatures, Minzu University of China, Beijing 100081, ChinaCollege of Computer Science, Inner Mongolia University, Hohhot 010021, ChinaKey Laboratory of Ethnic Language Intelligent Analysis and Security Management of MOE, Minzu University of China, Beijing 100081, ChinaTraditional Mongolian document layout analysis faces unique challenges due to its vertical writing system and complex structural arrangements. Existing methods often struggle with the directional nature of traditional Mongolian text and require substantial computational resources. In this paper, we propose a direction-aware lightweight framework that effectively addresses these challenges. Our framework introduces three key innovations: a modified MobileNetV3 backbone with asymmetric convolutions for efficient vertical feature extraction, a dynamic feature enhancement module with channel attention for adaptive multi-scale information fusion, and a direction-aware detection head with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><mo form="prefix">sin</mo><mi>θ</mi><mo>,</mo><mo form="prefix">cos</mo><mi>θ</mi><mo>)</mo></mrow></semantics></math></inline-formula> vector representation for accurate orientation modeling. We evaluate our method on TMDLAD, a newly constructed traditional Mongolian document layout analysis dataset, comparing it with both heavy ResNet-50-based models and lightweight alternatives. The experimental results demonstrate that our approach achieves state-of-the-art performance, with 0.715 mAP and 92.3% direction accuracy with a mean absolute error of only 2.5°, while maintaining high efficiency at 28.6 FPS using only 8.3 M parameters. Our model outperforms the best ResNet-50-based model by 3.6% in mAP and the best lightweight model by 4.3% in mAP, while uniquely providing direction prediction capability that other lightweight models lack. The proposed framework 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.https://www.mdpi.com/2076-3417/15/8/4594traditional Mongoliandocument layout analysisdirection-aware detectionasymmetric convolutionvector orientation representationlightweight model |
| spellingShingle | Chenyang Zhou Monghjaya Ha Licheng Wu Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis Applied Sciences traditional Mongolian document layout analysis direction-aware detection asymmetric convolution vector orientation representation lightweight model |
| title | Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis |
| title_full | Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis |
| title_fullStr | Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis |
| title_full_unstemmed | Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis |
| title_short | Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis |
| title_sort | direction aware lightweight framework for traditional mongolian document layout analysis |
| topic | traditional Mongolian document layout analysis direction-aware detection asymmetric convolution vector orientation representation lightweight model |
| url | https://www.mdpi.com/2076-3417/15/8/4594 |
| work_keys_str_mv | AT chenyangzhou directionawarelightweightframeworkfortraditionalmongoliandocumentlayoutanalysis AT monghjayaha directionawarelightweightframeworkfortraditionalmongoliandocumentlayoutanalysis AT lichengwu directionawarelightweightframeworkfortraditionalmongoliandocumentlayoutanalysis |