Publication Type : Journal
Publisher : Procedia Computer Science, Elsevier.
Source : Procedia Computer Science, Elsevier, Volume 93, p.469-477 (2016)
Keywords : Character recognition, Diffusion, Document layouts, Edge enhancements, Edge preserving, Heuristic methods, Image processing, Level Set, Location, Mathematical morphology, Morphological operations, Nonlinear diffusion, Numerical methods, Optical character recognition, Optical character recognition (OCR), Scanned document images, Separation
Campus : Amritapuri
School : School of Engineering
Center : Cyber Security
Department : CISAI
Year : 2016
Abstract : Text/Image region separation is the process of identifying location of various text and image regions in a scanned document image. This is particularly helpful in detecting the layout of a scanned document image. The text region thus obtained can be used for optical character recognition (OCR) operation. The text region can be used to label and train automatic layout learning system to detect locations of title, keywords, subheadings, paragraphs, image locations etc. In case of regular image and text boundaries, Profiling or morphological operations can be used for separating the text and image regions and to achieve correct document layout out detection. However, the real-world documents will have irregular boundaries and noise, the usual profile based methods and its heuristic often fails. This will lead to incorrect document layouts. This paper proposes to use edge enhancement diffusion and level set method for text/image region separation from scanned document images. The result obtained shows that the proposed method works when the document contain multiple images. The proposed method detects the layout of the scanned document even when the image and the text regions have irregular shape. © 2016 The Authors. Published by Elsevier B.V.
Cite this Research Publication : S. S. Kumar, Rajendran, P., Prabaharan, P., Dr. Soman K. P., J., M., and J., J., “Text/Image Region Separation for Document Layout Detection of Old Document Images Using Non-linear Diffusion and Level Set”, in Procedia Computer Science, 2016, vol. 93, pp. 469-477.