mlsanityData to 7 Oct 2026

Use neural networks to recognize students' handwritten letters and incorrect symbols

arXiv 2309.06221 ↗First posted 12 Sep 2023cs.CVcited by 0

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Abstract (arXiv)

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 36 references that are in the map. First posted on arXiv 12 Sep 2023.

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References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  6. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  7. 2014Going Deeper with Convolutionscited by 4,231
  8. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  9. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  10. 2020Global-aware Beam Search for Neural Abstractive Summarizationcited by 8
  11. 2022More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inferencecited by 8
  12. 2021Sub-GMN: The Neural Subgraph Matching Network Modelcited by 5
1 more
  1. 2023RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learningcited by 5

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