Summary of Base and Exponent Prediction in Mathematical Expressions Using Multi-output Cnn, by Md Laraib Salam et al.
Base and Exponent Prediction in Mathematical Expressions using Multi-Output CNN
by Md Laraib Salam, Akash S Balsaraf, Gaurav Gupta
First submitted to arxiv on: 20 Jul 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 The paper presents a simplified yet effective approach to predicting both the base and exponent from images of mathematical expressions using a multi-output Convolutional Neural Network (CNN). By training on 10,900 synthetically generated images containing exponent expressions, incorporating random noise, font size variations, and blur intensity to simulate real-world conditions, the proposed CNN model demonstrates robust performance with efficient training time. The experimental results indicate that the model achieves high accuracy in predicting the base and exponent values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a special type of artificial intelligence called a neural network to recognize mathematical expressions from images. This can be useful for people who want to find answers or solve problems quickly. However, making these networks work well requires a lot of computation power and data. The researchers found a way to make the process simpler and more efficient by training the network on many fake images that look like real ones. They tested their method and it worked really well! |
Keywords
* Artificial intelligence * Cnn * Neural network
