Summary of Integrating Canonical Neural Units and Multi-scale Training For Handwritten Text Recognition, by Zi-rui Wang
Integrating Canonical Neural Units and Multi-Scale Training for Handwritten Text Recognition
by Zi-Rui Wang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed recognition network leverages a novel three-dimensional attention module and global-local context information to tackle handwritten text recognition. Building upon connectionist temporal classification, hidden Markov model, and encoder-decoder methods, this work combines the strengths of these approaches to achieve state-of-the-art performance. The network consists of 3D blocks with different resolutions, which are fed into the attention module to generate sequential visual features. These features are then integrated with global-local context information to produce a well-designed representation. The model is trained using the CTC loss and cross-entropy loss, and experiments on Chinese and English handwritten text datasets demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new approach for recognizing handwritten text. It combines ideas from different machine learning methods to create a better way of doing this task. The approach uses a special kind of attention that looks at features in three dimensions, as well as information about the context of the writing. This helps the model learn to recognize text even if it’s not perfectly written. The researchers tested their method on several datasets and found that it works very well. |
Keywords
» Artificial intelligence » Attention » Classification » Cross entropy » Encoder decoder » Hidden markov model » Machine learning
