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Document Layout Analysis

Showmik Bhowmik

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English
Springer Verlag, Singapore
01 August 2023
Document layout analysis (DLA) is a crucial step towards the development of an effective document image processing system. In the early days of document image processing, DLA was not considered as a complete and complex research problem, rather just a pre-processing step having some minor challenges. The main reason for that is the type of layout being considered for processing was simple. Researchers started paying attention to this complex problem as they come across a large variety of documents. This book presents a clear view of the past, present, and future of DLA, and it also discusses two recent methods developed to address the said problem.
By:  
Imprint:   Springer Verlag, Singapore
Country of Publication:   Singapore
Edition:   1st ed. 2023
Dimensions:   Height: 235mm,  Width: 155mm, 
Weight:   163g
ISBN:   9789819942763
ISBN 10:   9819942764
Series:   SpringerBriefs in Computer Science
Pages:   86
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
Chapter 1. Introduction.-  Chapter 2. Binarization.- Chapter 3. Document Region Segmentation.- Chapter 4. Document Region Classification.-  Chapter 5. Case Study.- Chapter 6. Summary.    

Dr. Showmik Bhowmik is currently working as an assistant professor in the Department of Computer Science and Engineering at Ghani Khan Choudhury Institute of Engineering and Technology, a centrally funded technical institute under the Ministry of Education, Government of India. He has received his ME and PhD degree from Jadavpur University, India, from the Department of Computer Science and Engineering. During his PhD, Dr. Bhowmik was selected as Visvesvaraya Research Fellow under the Ministry of Electronics and Information Technology, Government of India. Dr. Bhowmik has more than 34 publications in SCI-indexed journals and conferences. His current areas of research are pattern recognition, image processing, offline handwriting recognition, machine learning, and deep learning.

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