Cascaded Dual-Inpainting Network for Scene Text
Guardado en:
| Publicado en: | Applied Sciences vol. 15, no. 14 (2025), p. 7742-7758 |
|---|---|
| Autor principal: | |
| Publicado: |
MDPI AG
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3233049791 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15147742 |2 doi | |
| 035 | |a 3233049791 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Liu, Chunmei | |
| 245 | 1 | |a Cascaded Dual-Inpainting Network for Scene Text | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Scene text inpainting is a significant research challenge in visual text processing, with critical applications spanning incomplete traffic sign comprehension, degraded container-code recognition, occluded vehicle license plate processing, and other incomplete scene text processing systems. In this paper, a cascaded dual-inpainting network for scene text (CDINST) is proposed. The architecture integrates two scene text inpainting models to reconstruct the text foreground: the Structure Generation Module (SGM) and Structure Reconstruction Module (SRM). The SGM primarily performs preliminary foreground text reconstruction and extracts text structures. Building upon the SGM’s guidance, the SRM subsequently enhances the foreground structure reconstruction through structure-guided refinement. The experimental results demonstrate compelling performance on the benchmark dataset, showcasing both the effectiveness of the proposed dual-inpainting network and its accuracy in incomplete scene text recognition. The proposed network achieves an average recognition accuracy improvement of 11.94% compared to baseline methods for incomplete scene text recognition tasks. | |
| 653 | |a Accuracy | ||
| 653 | |a Methods | ||
| 653 | |a Deep learning | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Corruption | ||
| 653 | |a Text structure | ||
| 653 | |a Neural networks | ||
| 653 | |a Semantics | ||
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 14 (2025), p. 7742-7758 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233049791/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233049791/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233049791/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |