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Handwriting is a natural way to communicate and record information. Machine Simulation to recognize off-line handwriting has opened new horizons to improve human-computer interface. This book presents superior approaches for character image pre-processing, untouched as well as touched character segmentation and feature extraction for the purpose of handwritten word recognition experiment. The first segmentation technique is based on the connected component analysis and is proposed to segment untouched characters in a word image and in the second technique, a heuristic vertical dissection based approach is proposed to segment touched characters in a word image. A fusion of two feature extraction techniques i.e Binarization and Projection Profile Techniques is used to evaluate the performance of the two variants of Artificial Neural Networks, namely, Feed Forward Back Propagation NN and Radial Basis Function NN in terms of accuracy, speed and computational complexity. To help the researchers, various techniques to optimize the training parameters of an ANN are evaluated and some common situations during BP Learning, with possible causes and potential remedies are also presented.
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Handwriting is a natural way to communicate and record information. Machine Simulation to recognize off-line handwriting has opened new horizons to improve human-computer interface. This book presents superior approaches for character image pre-processing, untouched as well as touched character segmentation and feature extraction for the purpose of handwritten word recognition experiment. The first segmentation technique is based on the connected component analysis and is proposed to segment untouched characters in a word image and in the second technique, a heuristic vertical dissection based approach is proposed to segment touched characters in a word image. A fusion of two feature extraction techniques i.e Binarization and Projection Profile Techniques is used to evaluate the performance of the two variants of Artificial Neural Networks, namely, Feed Forward Back Propagation NN and Radial Basis Function NN in terms of accuracy, speed and computational complexity. To help the researchers, various techniques to optimize the training parameters of an ANN are evaluated and some common situations during BP Learning, with possible causes and potential remedies are also presented.
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- VerlagScholars' Press
- Erscheinungsdatum2018
- ISBN 10 6202306858
- ISBN 13 9786202306850
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten212
- Kontakt zum HerstellerNicht verfügbar
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A Neural Approach to OCR System Design
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Choudhary AmitDr. Amit Choudhary is currently Associate Professor & Head, Department of Computer Science, Maharaja Surajmal Institute, New Delhi. He has done MCA, M.Tech., M.Phil.& Ph.D. in Computer Science and holds 15 years of expe. Bestandsnummer des Verkäufers 385940956
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A Neural Approach to OCR System Design
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Handwriting is a natural way to communicate and record information. Machine Simulation to recognize off-line handwriting has opened new horizons to improve human-computer interface. This book presents superior approaches for character image pre-processing, untouched as well as touched character segmentation and feature extraction for the purpose of handwritten word recognition experiment. The first segmentation technique is based on the connected component analysis and is proposed to segment untouched characters in a word image and in the second technique, a heuristic vertical dissection based approach is proposed to segment touched characters in a word image. A fusion of two feature extraction techniques i.e Binarization and Projection Profile Techniques is used to evaluate the performance of the two variants of Artificial Neural Networks, namely, Feed Forward Back Propagation NN and Radial Basis Function NN in terms of accuracy, speed and computational complexity. To help the researchers, various techniques to optimize the training parameters of an ANN are evaluated and some common situations during BP Learning, with possible causes and potential remedies are also presented. 212 pp. Englisch. Bestandsnummer des Verkäufers 9786202306850
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A Neural Approach to OCR System Design : Concept, Design, Implementation & Evaluation
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Handwriting is a natural way to communicate and record information. Machine Simulation to recognize off-line handwriting has opened new horizons to improve human-computer interface. This book presents superior approaches for character image pre-processing, untouched as well as touched character segmentation and feature extraction for the purpose of handwritten word recognition experiment. The first segmentation technique is based on the connected component analysis and is proposed to segment untouched characters in a word image and in the second technique, a heuristic vertical dissection based approach is proposed to segment touched characters in a word image. A fusion of two feature extraction techniques i.e Binarization and Projection Profile Techniques is used to evaluate the performance of the two variants of Artificial Neural Networks, namely, Feed Forward Back Propagation NN and Radial Basis Function NN in terms of accuracy, speed and computational complexity. To help the researchers, various techniques to optimize the training parameters of an ANN are evaluated and some common situations during BP Learning, with possible causes and potential remedies are also presented. Bestandsnummer des Verkäufers 9786202306850
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A Neural Approach to OCR System Design: Concept, Design, Implementation & Evaluation
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A Neural Approach to OCR System Design: Concept, Design, Implementation & Evaluation
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A Neural Approach to OCR System Design: Concept, Design, Implementation & Evaluation
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A Neural Approach to OCR System Design: Concept, Design, Implementation & Evaluation
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