EnsNet: Ensconce Text in the Wild
Authors: Shuaitao Zhang, Yuliang Liu, Lianwen Jin, Yaoxiong Huang, Songxuan Lai801-808
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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). |