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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.08017 |
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| _version_ | 1866912269732937728 |
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| author | Liu, Youjin Wang, Yu |
| author_facet | Liu, Youjin Wang, Yu |
| contents | In recent years, partial differential equation (PDE) systems have been successfully applied to the binarization of text images, achieving promising results. Inspired by the DH model and incorporating a novel image modeling approach, this study proposes a new weakly coupled PDE system for degraded text image binarization. In this system, the first equation is designed to estimate the background component, incorporating both diffusion and fidelity terms. The second equation estimates the foreground component and includes diffusion, fidelity, and binarization source terms. The final binarization result is obtained by applying a hard projection to the estimated foreground component. Experimental results on 86 degraded text images demonstrate that the proposed model exhibits significant advantages in handling degraded text images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08017 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Partial differential equation system for binarization of degraded document images Liu, Youjin Wang, Yu Computer Vision and Pattern Recognition Dynamical Systems In recent years, partial differential equation (PDE) systems have been successfully applied to the binarization of text images, achieving promising results. Inspired by the DH model and incorporating a novel image modeling approach, this study proposes a new weakly coupled PDE system for degraded text image binarization. In this system, the first equation is designed to estimate the background component, incorporating both diffusion and fidelity terms. The second equation estimates the foreground component and includes diffusion, fidelity, and binarization source terms. The final binarization result is obtained by applying a hard projection to the estimated foreground component. Experimental results on 86 degraded text images demonstrate that the proposed model exhibits significant advantages in handling degraded text images. |
| title | Partial differential equation system for binarization of degraded document images |
| topic | Computer Vision and Pattern Recognition Dynamical Systems |
| url | https://arxiv.org/abs/2503.08017 |