Yang, Z.; Zuo, S.; Zhou, Y.; He, J.; Shi, J. A Review of Document Binarization: Main Techniques, New Challenges, and Trends. Electronics2024, 13, 1394.
Yang, Z.; Zuo, S.; Zhou, Y.; He, J.; Shi, J. A Review of Document Binarization: Main Techniques, New Challenges, and Trends. Electronics 2024, 13, 1394.
Yang, Z.; Zuo, S.; Zhou, Y.; He, J.; Shi, J. A Review of Document Binarization: Main Techniques, New Challenges, and Trends. Electronics2024, 13, 1394.
Yang, Z.; Zuo, S.; Zhou, Y.; He, J.; Shi, J. A Review of Document Binarization: Main Techniques, New Challenges, and Trends. Electronics 2024, 13, 1394.
Abstract
Document image binarization is a challenging task, especially when it comes to text segmentation in degraded document images. The binarization, as a pre-processing step of optical character recognition (OCR), is one of the most fundamental and commonly used segmentation methods. It separates the foreground text from the background of the document image to facilitate subsequent image processing. In view of the different degradation degree of document image, researchers have proposed a variety of solutions. This paper reviews the main binarization techniques, including both traditional algorithms and deep learning-based algorithms. We also summarize some difficulties and challenges in the field of document image binarization. Here, we evaluate various image binarization techniques to identify shortcomings in current methods and provide some help for future research.
Keywords
degraded document images; binarization; threshold processing; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
