Summary of An Inclusive Review on Deep Learning Techniques and Their Scope in Handwriting Recognition, by Sukhdeep Singh et al.
An inclusive review on deep learning techniques and their scope in handwriting recognition
by Sukhdeep Singh, Sudhir Rohilla, Anuj Sharma
First submitted to arxiv on: 10 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 Deep learning algorithms have achieved remarkable success in various fields by combining raw inputs into intermediate feature layers. In particular, they have reached human-level performance in computer vision and pattern recognition domains. These architectures employ activation functions to perform computations between hidden and output layers. This paper presents a survey on deep learning studies in the handwriting recognition field, highlighting its promising advances despite ongoing challenges and limitations, such as inadequate labelled data availability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning is like a superpower for machines! It helps them learn from lots of information and get really good at tasks like recognizing patterns or reading handwriting. This paper looks at how deep learning has helped with handwriting recognition and what it can do better. It’s like trying to improve a machine’s skills so it can read and write just like us! |
Keywords
* Artificial intelligence * Deep learning * Pattern recognition
