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..
EnsNet: Ensconce Text in the Wild
Authors: Shuaitao Zhang, Yuliang Liu, Lianwen Jin, Yaoxiong Huang, Songxuan Lai801-808
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the Ens Net is essential to achieve a good performance. Moreover, our Ens Net can significantly outperform previous state-of-the-art methods in terms of all metrics. |
| Researcher Affiliation | Academia | Shuaitao Zhang, Yuliang Liu, Lianwen Jin, Yaoxiong Huang, Songxuan Lai School of Electronic and Information Engineering South China University of Technology |
| Pseudocode | No | The paper describes the methodology using prose and diagrams (Figure 2, Figure 3) but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The synthetic data are available at: https://github.com/HCIILAB/Scene-Text-Removal. |
| Open Datasets | Yes | However, such text dataset does not exist currently; thus, we constructed a synthetic dataset for evaluating the performance. In addition, we evaluated the performance on the ICDAR 2013 (Karatzas et al. 2013) dataset... The synthetic data are available at: https://github.com/HCIILAB/Scene-Text-Removal. |
| Dataset Splits | No | In our experiments, the training set consists of a total of 8000 images and the test set contains 800 images; all the training and test samples are resized to 512 × 512. |
| Hardware Specification | Yes | Ens Net is extremely fast, which can preform at 333 fps on an i5-8600 CPU device. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In our experiments, λe, λi, and λt are empirically set to 0.5, 50.0, and 25.0, respectively. All experiments use exactly the same settings (input size is set to 512 × 512). |