● | 技術名稱 Technology | |||||||||||||
● | 發明人 Inventor |
郭景明, 張立穎, | ||||||||||||
● | 所有權人 Asignee | 國立臺灣科技大學 | ||||||||||||
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點閱數:670 |
技術摘要: | ||||||||||||||||||||||||||||||||
Nowadays, document segmentation is one of the key problems in the eld of semantic segmentation. Although this demand applying deep learning approaches has undergone continuing advancement, convolutional neural networks are commonly limited at a xed resource budget. To tackle this problem, we present the high-eciency encoderdecoder model for pixel-wise document segmentation, namely EDSNet. The architecture of the encoder network is based on EcientNet. The decoder stage is composed of a set of convolutions and upsampling layers to full input resolution feature maps for pixel-wise classication. To overcome the contour of document features and multi-level feature capability, this paper proposes an edge supervision network and Densely Joint Pyramid Module (DJPM) to improve the performance. As a result, the proposed document page segmentation method achieves 91.22% using IoU evaluation on the dataset of RDCL 2017. Moreover, a large-scale dataset for document segmentation, called PPSD2020, was released with this paper for open access. |
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