文件分割解析在語義分割技術是具有挑戰的任務之一，其原因為文件影像具備大量的圖片、表格、文字與背景等複雜的結構資訊，導致現今語義分割技術在處理複雜的文件結構影像上仍有改善及進步的空間。文件語義分割技術目前所面臨的瓶頸有三: (1)文件結構分割結果之破損與分類錯誤 (2)無法分割文件之小物件與(3)不同結構物件邊界之切割輪廓不平整。
本論文針對文件分割應用提出一基於密集多尺度金字塔模型之對稱性編碼與解碼器架構以提升文件分割效能。為了解決文件分割所造成的破損與分類錯誤，本論文採用高效率對稱性編解碼器架構來補償空間資訊的流失與降低網路之參數量。另外，本論文中設計密集多尺度金字塔模塊(DMPM)與特徵融合模塊(FFM)來強化網路之視野度，以利網路有效解決小物件切割失敗的問題。同時，本論文設計邊緣監督網路架構用以改善文件的輪廓分割結果，並透過多任務學習方式使網路在文件語義分析與邊緣資訊共同學習來提升網路整體效能。在實驗結果方面，本論文採用公開切割競賽資料庫(RDCL2017)進行測試，並與前人的方法比較。從結果顯示所提出的網路架構其效能在均交並比(mIoU)上可高達92.02%。而在公開表格資料庫(Marmot)中，本論文所提出的方法在召回率與精準度各為80.3%與82.7%，與前人的方法比較上也皆是相當突出的。綜合上述實驗結果可得知本論文中所設計的網路架構能克服文件切割中的問題並有效提升其切割效能。

Document segmentation is one of the most challenging tasks in semantic segmentation. Since page images comprise a large number of figures, tables, texts, and the background, the segmentation results using state-of-the-art approaches were dissatisfactory due to these complex structures. Consequently, three bottlenecks in document segmentation are (1) misclassification of structural data or incomplete segmented region, (2) missing tiny objects, and (3) poor boundary delineations of the objects among structures.
In this thesis, a densely multi-scale pyramid module embedded with the symmetric encoder-decoder structure is proposed to largely improve the performance for document segmentation. To tackle the problem of misclassification and incomplete segmented region, an efficient network with symmetric encoder-decoder structure is designed to compensate the spatial information loss and reduce the number of parameters. Besides, Densely Multi-scale Pyramid Module (DMPM) and Feature Fusion Module (FFM) are designed and employed into the network to extend the multiple effective Fields-of-View (FoV), so that the issue of missing tiny objects can be resolved. Furthermore, the edge supervision network is proposed to enhance the contour delineations of the document objects and leverage up the network performance by multi-task learning. The experimental results show that the proposed network outperforms state-of-the-art methods on the public dataset of RDCL2017. The proposed method reaches up to 92.02% in mIoU for performance evaluation. And for another public dataset of Marmot, the proposed network achieves 80.3% and 82.7% in recall and precision, respectively. Accordingly, the network proposed in this thesis can be considered as a very competitive candidate for the applications of document segmentation.

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