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Historical Text Image Enhancement Using Image Scaling and Generative Adversarial Networksopen access

Authors
Khan, Sajid UllahUllah, ImdadKhan, FaheemLee, YoungmoonUllah, Shahid
Issue Date
Apr-2023
Publisher
MDPI
Keywords
text image enhancement; wavelet transform; generative adversarial network; machine learning
Citation
SENSORS, v.23, no.8
Journal Title
SENSORS
Volume
23
Number
8
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/87962
DOI
10.3390/s23084003
ISSN
1424-8220
Abstract
Historical documents such as newspapers, invoices, contract papers are often difficult to read due to degraded text quality. These documents may be damaged or degraded due to a variety of factors such as aging, distortion, stamps, watermarks, ink stains, and so on. Text image enhancement is essential for several document recognition and analysis tasks. In this era of technology, it is important to enhance these degraded text documents for proper use. To address these issues, a new bi-cubic interpolation of Lifting Wavelet Transform (LWT) and Stationary Wavelet Transform (SWT) is proposed to enhance image resolution. Then a generative adversarial network (GAN) is used to extract the spectral and spatial features in historical text images. The proposed method consists of two parts. In the first part, the transformation method is used to de-noise and de-blur the images, and to increase the resolution effects, whereas in the second part, the GAN architecture is used to fuse the original and the resulting image obtained from part one in order to improve the spectral and spatial features of a historical text image. Experiment results show that the proposed model outperforms the current deep learning methods.
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Khan, Faheem
College of IT Convergence (컴퓨터공학부(컴퓨터공학전공))
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