TY - GEN
T1 - LK-Net
T2 - 27th International Conference on Pattern Recognition, ICPR 2024
AU - Shi, Qijun
AU - Zhan, Hongjian
AU - Li, Yangfu
AU - Zou, Weijun
AU - Li, Huasheng
AU - Pal, Umapada
AU - Lu, Yue
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Various types of degradation in document images, such as blurring, shadow, and physical wear and tear, significantly impact the effectiveness of downstream tasks in multimedia applications. The need for document image enhancement arises from the urgent need to improve the legibility and quality of these images, which are integral for accurate Optical Character Recognition(OCR), information retrieval, document analysis, etc. This paper introduces a novel and simple approach employing Large Kernel Convolutional Networks (ConvNets) for document image enhancement, capitalizing on their ability to encapsulate expansive contextual information to improve image quality. Extensive experimental evaluations across multiple benchmarks have demonstrated that our method achieves state-of-the-art (SOTA) while maintaining low computational cost. Code and pre-trained models are available at https://github.com/qijunshi/LKNet.
AB - Various types of degradation in document images, such as blurring, shadow, and physical wear and tear, significantly impact the effectiveness of downstream tasks in multimedia applications. The need for document image enhancement arises from the urgent need to improve the legibility and quality of these images, which are integral for accurate Optical Character Recognition(OCR), information retrieval, document analysis, etc. This paper introduces a novel and simple approach employing Large Kernel Convolutional Networks (ConvNets) for document image enhancement, capitalizing on their ability to encapsulate expansive contextual information to improve image quality. Extensive experimental evaluations across multiple benchmarks have demonstrated that our method achieves state-of-the-art (SOTA) while maintaining low computational cost. Code and pre-trained models are available at https://github.com/qijunshi/LKNet.
KW - Document Binarization
KW - Document Deblurring
KW - Document Enhancement
UR - https://www.scopus.com/pages/publications/85212297800
U2 - 10.1007/978-3-031-78305-0_18
DO - 10.1007/978-3-031-78305-0_18
M3 - 会议稿件
AN - SCOPUS:85212297800
SN - 9783031783043
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 275
EP - 290
BT - Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings
A2 - Antonacopoulos, Apostolos
A2 - Chaudhuri, Subhasis
A2 - Chellappa, Rama
A2 - Liu, Cheng-Lin
A2 - Bhattacharya, Saumik
A2 - Pal, Umapada
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 December 2024 through 5 December 2024
ER -