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In our work, we propose an end-to-end encoder-decoder network for complex document layout segmentation. The proposed multi-scale feature extraction network with two parallel branches is applied to further process the feature maps, where one branch enriches the multi-scale information of the feature maps by building feature pyramids, another branch is introduced to capture the dependencies between different locations and integrate long-range context information. Moreover, we merge the outputs of the two branches to enhance the feature representation so as to further improve segmentation accuracy. Experimental results on datasets of PubLayNet and DSSE-200 demonstrate the effectiveness of our proposed method, which yields pixel-wise accuracy of above 99%.

Complex Document Layout Segmentation Based on An Encoder-Decoder Architecture