Abstract:
Text document segmentation is one of the essential steps in text document
recognition and extraction systems. The existing image segmentation methods are not
much reliable for text with colour gradient and texture background. Also, long
processing time of existing methods is unfavorable. Thus, aiming to have reliable
segmentation and less processing time, a new local thresholding method is proposed
and its performance is tested in this study.
The proposed method is based on pixel intensity magnification concept. In
proposed algorithm, the input image is enhanced by edge sharpening. Then, the image
is divided into multiple local windows by using non-overlapping. The magnification
factor for each non-overlapping local window is calculated based on minimum
intensity, maximum intensity, range and the number of dominant intensities in the
corresponding local window. For segmenting pixels in each local window,
magnification factor is used.
The performance of proposed algorithm is measured in terms of segmenting
accuracy and processing time. The tested images include different types such as text
in uniform colour, text in multicolour, text in gradient background, text in national ID,
text with watermark, highlight text, text in light illumination, text in passport, text in
bank card. Also, the performances of Otus’ and Niblack’s methods are tested with the
same images.
The proposed method gives the better results for most images with maximum
accuracy of 100% and lowest accuracy of 80%. The highest efficiency of Otsu’s
method is 100% and its lowest accuracy is 0%. Since Otsu’s method loses all data
sometimes. For Niblack’s method, it gives 100% accuracy only for text with simple
colour background while its lowest accuracy is 10%. The proposed system’s average
accuracy is higher than Otus’ and Niblack’s average accuracy.
