Summary of Multi-cell Decoder and Mutual Learning For Table Structure and Character Recognition, by Takaya Kawakatsu
Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition
by Takaya Kawakatsu
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper presents an innovative approach to extracting table contents from documents like scientific papers and financial reports. By recognizing both table structure and cell contents simultaneously, end-to-end models can achieve performance on par with state-of-the-art systems that rely on external character recognition tools. The proposed method also enables the recognition of long tables with hundreds of cells by introducing local attention mechanisms. However, traditional approaches recognize table structures in a one-way direction from headers to footers and cell contents independently, limiting opportunities for retrieving useful information from neighboring cells. To address this limitation, the authors introduce a multi-cell content decoder and bidirectional mutual learning mechanism to improve end-to-end performance. Experimental results demonstrate comparable performance to state-of-the-art models on two large datasets, even for complex tables with numerous cells. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to understand tables in documents like scientific papers and financial reports. Right now, computers can recognize the structure of tables, but they need help recognizing what’s inside each cell. The researchers propose a new way to do this by looking at multiple cells together, rather than just one cell at a time. They also create a way for computers to learn from each other as they try to understand tables. This makes it possible for computers to recognize really long tables with lots of information. |
Keywords
» Artificial intelligence » Attention » Decoder
