Text Gestalt: Stroke-Aware Scene Text Image Super-resolution
Authors: Jingye Chen, Haiyang Yu, Jianqi Ma, Bin Li, Xiangyang Xue285-293
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on Text Zoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science, Fudan University 2The Hong Kong Polytechnic University |
| Pseudocode | No | The paper describes the method verbally but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/FudanVI/FudanOCR. |
| Open Datasets | Yes | The datasets used in this paper are as follows: Text Zoom (Wang et al. 2020) The images in Text Zoom originate from Real SR (Cai et al. 2019) and SR-RAW (Zhang et al. 2019). ...IC15 (Karatzas et al. 2015)... Degraded-IC13 is constructed based on IC13-HCCR (Yin et al. 2013)... After pretrained on Synth90k (Jaderberg et al. 2016) and Synth Text (Gupta, Vedaldi, and Zisserman 2016)... |
| Dataset Splits | Yes | Text Zoom contains 17, 367 LR-HR pairs for training and 4, 373 pairs for testing. In terms of different focal lengths of digital cameras, the test set is divided into three subsets, including 1, 619 LR-HR pairs for the easy subset, 1, 411 LR-HR pairs for the medium subset, and 1, 343 LR-HR pairs for the hard subset. ... We employ CRNN for validation. |
| Hardware Specification | Yes | All experiments are conducted on one NVIDIA GTX 1080Ti GPU with 11GB memory. |
| Software Dependencies | No | The paper mentions "Our model is implemented in Py Torch" but does not specify a version number for PyTorch or any other software dependencies with their versions. |
| Experiment Setup | Yes | The model is trained using Adam (Kingma and Ba 2014) optimizer with learning rate set to 10 4. The batch size is set to 16. |