Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Accurate Structured-Text Spotting for Arithmetical Exercise Correction
Authors: Yiqing Hu, Yan Zheng, Hao Liu, Dequang Jiang, Yinsong Liu, Bo Ren686-693
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that AEC yields a 93.72% correction accuracy on 40 kinds of mainstream primary arithmetical exercises. |
| Researcher Affiliation | Industry | Yiqing Hu, Yan Zheng, Hao Liu, Deqiang Jiang, Yinsong Liu, Bo Ren Youtu Lab, Tencent EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'We will release this dataset soon' regarding AEC-5k, but does not provide a specific statement or link for the release of source code for the described methodology. |
| Open Datasets | No | The paper states, 'We will release these datasets soon.' referring to AEC-5k and the 600k synthetic corpus, but it does not provide a concrete link, DOI, or specific repository name for immediate access to these datasets. |
| Dataset Splits | No | The paper mentions '5,000 images for training and 300 images for testing' for AEC-5k, but does not specify a validation dataset split. |
| Hardware Specification | Yes | Basing on Pytorch (Paszke et al. 2017), we implement all benchmarks on a regular platform with 8 Nvidia P40 GPUs and 64GB memory. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | We adapt the Adam optimizer with learning rate 2.5 10 4 for optimization. We adapt the SGD optimizer with learning rate 0.1 for optimization. The learning rate halves after 300k iterations, and halves again after each 100k iterations. |