A Survey on Document Image Binarization Techniques
Title | A Survey on Document Image Binarization Techniques |
Publication Type | Conference Paper |
Year of Publication | 2015 |
Authors | Lokhande, S. S., Dawande, N. A. |
Conference Name | 2015 International Conference on Computing Communication Control and Automation |
Date Published | Feb. 2015 |
Publisher | IEEE |
ISBN Number | 978-1-4799-6892-3 |
Keywords | adaptive contrast method, background text segmentation, degraded document image, distortion, document image analysis, document image binarization, document image binarization techniques, document image processing, document image recognition, foreground text segmentation, image contrast, Image edge detection, image recognition, image segmentation, Mathematical model, Measurement, performance evaluation metrics, pubcrawl170111, Segmentation, text analysis |
Abstract | Document image binarization is performed to segment foreground text from background text in badly degraded documents. In this paper, a comprehensive survey has been conducted on some state-of-the-art document image binarization techniques. After describing these document images binarization techniques, their performance have been compared with the help of various evaluation performance metrics which are widely used for document image analysis and recognition. On the basis of this comparison, it has been found out that the adaptive contrast method is the best performing method. Accordingly, the partial results that we have obtained for the adaptive contrast method have been stated and also the mathematical model and block diagram of the adaptive contrast method has been described in detail. |
URL | https://ieeexplore.ieee.org/document/7155946 |
DOI | 10.1109/ICCUBEA.2015.148 |
Citation Key | lokhande_survey_2015 |
- image contrast
- text analysis
- Segmentation
- pubcrawl170111
- performance evaluation metrics
- Measurement
- Mathematical model
- image segmentation
- image recognition
- Image edge detection
- adaptive contrast method
- foreground text segmentation
- document image recognition
- document image processing
- document image binarization techniques
- document image binarization
- document image analysis
- distortion
- degraded document image
- background text segmentation