Optimal Handwriting Recognition Using Soft Computing Techniques

This book critically reviews the various soft computing techniques employed in handwriting recognition and presents recognition accuracy achievements available in the literature. With advancements in the areas of artificial intelligence and machine learning, the expectation and challenges in handwriting recognition have become more and more demanding. The focus of this book is to explore the various steps involved in a handwriting recognition system such as pre-processing, feature extraction, feature selection and classification. Soft computing techniques such as neural network, fuzzy logic, genetic algorithm and neuro-fuzzy are applied in the recognition process. Some attempts based on hybrid feature extraction, GA based feature subset selection, ranking based feature selection methods, and optimization of learning parameters are also employed for further improvement in the recognition accuracy.

Autorentext

Dr Savita Ahlawat is currently working as a Reader (CSE Dept.) at MSIT, New Delhi. She has done B.E., M.Tech.(IT) & Ph.D. (CSE) and holds 15 years of teaching experience. She has published around 25 papers in international journals and conferences. Her research interests include Pattern Recognition, Machine Learning, Data Science & Computer Vision.