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.