Summary of Synthesizing Realistic Data For Table Recognition, by Qiyu Hou et al.
Synthesizing Realistic Data for Table Recognition
by Qiyu Hou, Jun Wang, Meixuan Qiao, Lujun Tian
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel method for synthesizing annotation data tackles the limitations of current table data annotation methods and random table data synthesis approaches by leveraging the structure and content of existing complex tables. The approach focuses on replicating authentic styles found in a target domain, such as Chinese financial announcements. By developing an extensive dataset in this domain, researchers can train deep learning-based end-to-end table recognition models with improved performance. Additionally, the paper establishes a benchmark for real-world complex tables and validates its method’s practicality and effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to create fake table data that looks like real financial reports from China. The goal is to make it easier to train computers to recognize tables in these reports. To do this, the researchers used actual tables from Chinese financial announcements as templates to generate new tables that look similar. They also created a big dataset of annotated tables for training machine learning models. This allowed them to test how well different models could recognize tables with multiple cells that span across rows and columns. The results show that using this synthetic data improves the performance of these models. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Synthetic data
