Browsing by Author "Rani, U"

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    Comparative Analysis of Salt and Pepper Removal Techniques for Binary Images
    (Springer, 2020) Rani, U; Kaur, A; Josan, G.
    Binarization is the most important step in the OCR system that converts the gray level or colored images into bi-level form. In the case of degraded images, results after binarization mostly contain noises. Salt and pepper noise of different sizes is the most prevalent noise in binary images. For the better results of OCR process, it is necessary to denoise image before proceeding to the next stage. This paper conducts experiments with different existing salt and pepper noise removal methods such as median filter-based techniques and kFill algorithm-based techniques for binary document images. The statistical measures, namely, PSNR, SSIM, and EPI are used to evaluate the performance. � 2020, Springer Nature Singapore Pte Ltd.
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    Modified Sauvola binarization for degraded document images
    (Elsevier, 2020) Kaur, A; Rani, U; Josan, G.S.
    The binarization of historical documents is a difficult job due to the presence of many degradations. Many existing local binarization techniques use certain manually adjusted parameters. The output of these techniques is much dependent on the value of these parameters. One of such parameters is window size which is kept fixed for the whole text image. The fixed window size will not be able to perform well for images having variable stroke widths and text sizes. The proposed binarization technique (Modified Sauvola) is the modification of state of art Sauvola's binarization technique. It automatically computes window size dynamically across the image pixel to pixel using the stroke width transform (SWT). This led to reduction in number of manually adjusted parameters. The results are compared with the nine existing techniques using the quantitative measures: FM, PSNR, NRM, MPM, and DRD. The results show that the proposed method outperforms existing methods for images having variable stroke widths and text sizes. - 2020 Elsevier Ltd