| dc.description.abstract | The recent era of digitization is expected to be digitized many old important documents
which are degraded due to various reasons. Binarizing Degraded Document Image for
Text Extraction is a conversation of document color image to binary image. Document images have mostly two classes: background and text. It can also be considered as a
text retrieval procedure as it extracts text from a degraded document. Degraded document
image binarization have many challenges like huge text intensity variation, background
contrast variation, bleed through, text size or stroke width variation in a single image,
highly overlapped background and foreground intensity ranges etc. Many approaches are
available for document image binarization, but none can handle all kind of degradation
at the same time. Mostly, a combination of global and/or local thresholding along with
various preprocessing as well as postprocessing techniques are used for document image
binarization to handle most of the challenges. The approach proposed in this thesis is
basically divided into three stages: preprocessing, Text-Area detection, post-processing.
Preprocessing employs PCA to convert image from RGB to Gray, followed by gamma
correction that enhances the contrast of the image. Contrast-enhanced image is filtered
with DoG (Difference of Gaussian) filter to boost local features of a text, followed by
equalization. Next stage involves identifying Text-Area. A Rough set based edge detection
technique is used to find closed boundary around texts, which results into locating Text-
Area along with some non-text area detected as text. Text is detected by applying logical
operators on preprocessed image and edge detected image. Postprocessing technique takes
care of false positives and false negative based on intensity values of preprocessed and
gray image. The algorithm is also expected to be independent of the script. To demonstrate
this, the algorithm is tested on Gujarati degraded document images. The Performance
is evaluated based on various quantitative measures like Distance Reciprocal Distortion
(DRD), Peak Signal-to-Noise Ratio (PSNR), F-Measure, and pseudo F-measure and It is
compared with the state-of-the-art (SOTA) method. The proposed approach is close to the
SOTA methods based on performance. It is able to binarize without losing text in some of the very challenging images, where state-of-the-art methods lose the text. | |
