Cascaded Dual-Inpainting Network for Scene Text

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...

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Main Author: Chunmei Liu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/14/7742
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author Chunmei Liu
author_facet Chunmei Liu
author_sort Chunmei Liu
collection DOAJ
description 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.
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spelling doaj-art-4be5a3781fdb4c848a77b8082d4c1d792025-07-25T13:12:13ZengMDPI AGApplied Sciences2076-34172025-07-011514774210.3390/app15147742Cascaded Dual-Inpainting Network for Scene TextChunmei Liu0School of Computer Science and Technology, Tongji University, Shanghai 201804, ChinaScene 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.https://www.mdpi.com/2076-3417/15/14/7742scene text processingincomplete scene textscene text inpaintingscene text structure extractionincomplete scene text recognition
spellingShingle Chunmei Liu
Cascaded Dual-Inpainting Network for Scene Text
Applied Sciences
scene text processing
incomplete scene text
scene text inpainting
scene text structure extraction
incomplete scene text recognition
title Cascaded Dual-Inpainting Network for Scene Text
title_full Cascaded Dual-Inpainting Network for Scene Text
title_fullStr Cascaded Dual-Inpainting Network for Scene Text
title_full_unstemmed Cascaded Dual-Inpainting Network for Scene Text
title_short Cascaded Dual-Inpainting Network for Scene Text
title_sort cascaded dual inpainting network for scene text
topic scene text processing
incomplete scene text
scene text inpainting
scene text structure extraction
incomplete scene text recognition
url https://www.mdpi.com/2076-3417/15/14/7742
work_keys_str_mv AT chunmeiliu cascadeddualinpaintingnetworkforscenetext