Summary of Hand: Hierarchical Attention Network For Multi-scale Handwritten Document Recognition and Layout Analysis, by Mohammed Hamdan et al.
HAND: Hierarchical Attention Network for Multi-Scale Handwritten Document Recognition and Layout Analysis
by Mohammed Hamdan, Abderrahmane Rahiche, Mohamed Cheriet
First submitted to arxiv on: 25 Dec 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 In this paper, researchers tackle handwritten document recognition (HDR), a challenging task in computer vision due to varying writing styles and complex layouts. They introduce HAND, an end-to-end architecture that simultaneously recognizes text and analyzes layout without segmentation. This model combines advanced convolutional encoding, multi-scale adaptive processing, hierarchical attention decoding, and memory-augmented attention mechanisms for efficient feature extraction and document analysis. The authors also fine-tune a pre-trained mT5 model for post-processing refinement on ancient manuscripts. Evaluations on the READ 2016 dataset show HAND’s superior performance, reducing CER by up to 59.8% for line-level recognition and 31.2% for page-level recognition compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Handwritten document recognition is a tough problem in computer vision because people write differently and documents have weird layouts. Researchers tried to solve this as two separate problems, but it didn’t work well. This paper introduces HAND, a new way to recognize text and figure out the layout at the same time without breaking the document into pieces. The model uses special encoding, processing, and attention mechanisms to make sense of complex documents. They also use an existing AI model to help with post-processing. The results on a special dataset show that HAND is really good, reducing errors by up to 59.8% for single lines and 31.2% for whole pages compared to other methods. |
Keywords
» Artificial intelligence » Attention » Cer » Feature extraction
