Abstract
Text extraction from blurred images is a difficult task in the field of
computer vision. Traditional image processing methods often fail to accurately extract
text from images with low resolution or high levels of noise. In the last few years, NLP
techniques have been applied to improve the accuracy of text extraction from blurred
images. This book chapter explores the use of NLP-based post-processing techniques
to improve the quality of text extraction from blurred images. The chapter first
provides an overview of traditional text extraction methods and the challenges
associated with extracting text from blurred images. It then discusses the use of NLP
techniques for improving the accuracy of text extraction. The chapter also explores the
use of machine learning algorithms, such as convolutional neural networks, to enhance
the performance of NLP-based post-processing techniques. Finally, the chapter
provides a case study demonstrating the effectiveness of NLP-based post-processing
techniques in improving text extraction from blurred images.
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