Document Layout Analysis Via Positional Encoding
- Resource Type
- Conference
- Authors
- Zhou, Ejian; Wu, Xingjiao; Xiao, Luwei; Du, Xiangcheng; Ma, Tianlong; He, Liang
- Source
- 2022 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2022 IEEE International Conference on. :1156-1160 Oct, 2022
- Subject
- Computing and Processing
Signal Processing and Analysis
Analytical models
Computer vision
Text analysis
Image coding
Layout
Predictive models
Maintenance engineering
Document layout analysis
position-encoding
bounding box
deep learning
- Language
- ISSN
- 2381-8549
Document layout analysis plays a vital role in computer vision research. Current document layout analysis methods mostly use pixel-based classification for document layout analysis. However, the method based on pixel classification is insufficient for maintaining the continuity of the classification area. In this paper, we propose a document layout analysis method based on positional encoding and bounding box specification. We maintain the continuity of the analysis area by constructing a document layout analysis framework based on the bounding box. In addition, we also integrate a positional encoding module in the framework to maintain the detailed information in the document layout analysis and modeling process. Experimental results prove that our proposed method has achieved state-of-the-art results.