Overview

In recent years there have been significant developments in the area of text recognition and document analysis. Measuring the relevant features contained in text is an important task in the recognition process, as the performance of the recognition system depends on the quality of features that are being extracted. As the volume of data keeps growing, it has become increasingly challenging to extract useful information from these sets. Feature Extraction and Classification Techniques for Text Recognition is a collection of innovative research on the fusion and hybridization of various features and classifiers for document analysis and recognition. While highlighting topics including adaptive boosting, writer identification, and signature verification, this book is ideally designed for academicians, researchers, industry professionals, developers, analysts, forensics specialists, scholars, and students seeking current research on the advancements and developing methods in document analysis and text recognition.

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9781799824077

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Munish Kumar received his Master's degree in Computer Science & Engineering from Thapar Institute of Engineering & Technology, Patiala, India in 2008. He received his Ph.D. degree from Thapar Institute of Engineering & Technology, Patiala, India in 2015. He started his career as an Assistant Professor in Computer Applications at Jaito centre of Punjabi University, Patiala. Presently, he is working as Assistant Professor in Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India. His research interests include Character Recognition, Handwriting Recognition and Pattern Recognition.

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