| Title | Docscanner: document location and enhancement based on image segmentation |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Shan, Ziqi, Wang, Yuying, Wei, Shunzhong, Li, Xiangmin, Pang, Haowen, Zhou, Xinmei |
| Conference Name | 2022 18th International Conference on Computational Intelligence and Security (CIS) |
| Date Published | dec |
| Keywords | composability, compositionality, Computational Intelligence, Computational modeling, cryptography, document processing, Document Scanner, Entropy, image segmentation, pubcrawl, security, semantic segmentation |
| Abstract | Document scanning aims to transfer the captured photographs documents into scanned document files. However, current methods based on traditional or key point detection have the problem of low detection accuracy. In this paper, we were the first to propose a document processing system based on semantic segmentation. Our system uses OCRNet to segment documents. Then, perspective transformation and other post-processing algorithms are used to obtain well-scanned documents based on the segmentation result. Meanwhile, we optimized OCRNet's loss function and reached 97.25 MIoU on the test dataset. |
| DOI | 10.1109/CIS58238.2022.00028 |
| Citation Key | shan_docscanner_2022 |
Docscanner: document location and enhancement based on image segmentation