Publication Type : Book Chapter
Publisher : Lecture Notes in Computational Vision and Biomechanics
Source : Lecture Notes in Computational Vision and Biomechanics, Volume 30, Pages 221-233, 2019
Keywords : Foxing effect, Local adaptive binarization, Nonuniform illumination, Optical character recognition, Pen and scratch marksPreprocessing, Stain marks
Campus : Mysuru
School : School of Arts and Sciences
Department : Computer Science
Year : 2019
Abstract : The field of Document Image Processing has encountered sensational development and progressively across the board relevance lately. Luckily, propels in PC innovation have kept pace with the fast development in the volume of picture information in different applications. One such utilization of Document picture preparing is OCR (Optical Character Recognition). Pre-preparing is one of the pre-imperative stages in the handling of record pictures which changes the archive to a frame reasonable for ensuing stages. In this paper, various preprocessing techniques are proposed for the enhancement of degraded document images. The algorithms implemented are adept at handling variety of noises that include foxing effect, illumination correction, show through effect, stain marks, and pen and other scratch marks removal. The techniques devised works based on noise degradation models generated from the attributes of noisy pixels which are commonly found in degraded or ancient document images. Further, these noise models are employed for the detection of noisy regions in the image to undergo the enhancement process. The enhancement procedures employed include the local normalization, convolution using central measures like mean and standard deviation, and Sauvola’s adaptive binarization technique. The outcomes of the preprocessing procedure is very promising and are adaptable to various degraded document scenarios. © Springer Nature Switzerland AG 2019.
Cite this Research Publication : Shobha Rani, N., Sajan Jain, A., Kiran, H.R., "A unified preprocessing technique for enhancement of degraded document images," ) Lecture Notes in Computational Vision and Biomechanics, 30, pp. 221-233, 2019.