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 | Archive PDF | Plain Text | 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. |