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Abstract In this thesis, we propose two adaptive and hybrid methods for binarization of historical document images. The rst method uses Otsu multilevel algorithm, and depends on the image contrast and the estimated stroke width. This method is proposed for binarization of historical document images suering from various types of degradation, such as non-uniform background, faint text, low contrast, stain, bleed-through, and shadow. Solving all these problems eectively is a challenge. Focus on noise elimination may cause loss of faint text. On the contrary, faint or low contrast text extraction may produce noisy images. Therefore, we classied the investigated images into two groups using a suggested factor. The rst group includes uniform background images that may contain faint text or shadow, while the second group includes non-uniform background images that may suer from stain, bleed-through, shadow or faint text. To extract this factor, the background of the investigated image is initially estimated, then global Otsu multilevel is applied and dividing it into three regions. The dierence between the average intensities of the darkest and brightest regions is an indicator of the image class. For each group, an adaptive and hybrid binarization technique is suggested. For the rst group, global Otsu is applied to the grayscale image and the stroke width is estimated. Areas that are more likely still contain missing text are identied adaptively ... and binarized separately using a pseudo-local version of Otsu multilevel method, and lost text recovered based on the stroke width. Faint text, shadow or background noise are distinguished based on the image contrast. The clarity of text is increased by using a dynamic window size for local binarization (Niblack method is used for thin pen stroke text and Otsu otherwise). For the second group, non-uniform background and most of stain and bleeding-through are removed by normalization, then global Otsu is applied and the stroke width is estimated. Text lost during normalization is restored. The remaining stain and bleed-through objects are detected depending on estimated stroke width, then they are locally binarized. Finally, a post-processing step based on the estimated stroke width is applied to remove the shadow. The proposed method is evaluated using seven databases DIBCO09, H-DIBCO’10, DIBCO’11, H-DIBCO’12, DIBCO’13, H-DIBCO’14, and H-DIBCO’16. The average F-measure for each database 90.7%, 89.1%, 88.9%, 88.7%, 88.9%, 93.3%, and 89.4% respectively. The second proposed method is suitable for normal illuminated document images that incorporate the advantages of Otsu and spectral clustering algorithm. To overcome the noise problem, a preprocessing step is applied to the document image. After that, the resulted image is binarized using Otsu producing a binary image. As a nal step, the spectral clustering algorithm is locally applied on the original image, with the aid of the binary image, to retrieve faint text. The proposed design of spectral clustering provides a signicant reduction of the similarity matrix computing time and size used, without aecting the quality of clustering. The proposed method is evaluated using the uniform background images taken from DIBCO09, H-DIBCO’10, DIBCO’11, H-DIBCO’12, DIBCO’13, H-DIBCO’14. |