Summary of Bidirectional Trained Tree-structured Decoder For Handwritten Mathematical Expression Recognition, by Hanbo Cheng et al.
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition
by Hanbo Cheng, Chenyu Liu, Pengfei Hu, Zhenrong Zhang, Jiefeng Ma, Jun Du
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 paper proposes a novel approach for handwritten mathematical expression recognition (HMER) by incorporating bidirectional context information into tree decoders. Existing methods have limitations in utilizing this information during inference and generalizing to different types of decoders. The authors introduce the Mirror-Flipped Symbol Layout Tree (MF-SLT) and Bidirectional Asynchronous Training (BAT) structure, which extends bidirectional training to tree decoders for more effective learning. Additionally, they analyze the impact of visual and linguistic perception on HMER models and propose the Shared Language Modeling (SLM) mechanism to enhance robustness and generalization. Experimental results demonstrate state-of-the-art performance on multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to recognize handwritten math problems. It gets better at recognizing these problems by using information from both sides of the equation, not just one side like before. The new approach is good for tree-shaped decoders, which are better at understanding complex math expressions. The researchers also found that giving the computer more training data helps it become even better at recognizing math problems. They tested their approach on several datasets and got the best results yet. |
Keywords
» Artificial intelligence » Generalization » Inference
