Seller: Books Puddle, New York, NY, U.S.A.
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Published by Scholars' Press Feb 2018, 2018
ISBN 10: 6202306858 ISBN 13: 9786202306850
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketTaschenbuch. Condition: Neu. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 212 pp. Englisch.
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Add to basketPaperback. Condition: Brand New. 212 pages. 8.66x5.91x0.48 inches. In Stock.
Published by Scholars' Press Feb 2018, 2018
ISBN 10: 6202306858 ISBN 13: 9786202306850
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: 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.
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Add to basketTaschenbuch. Condition: 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.
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Add to basketCondition: 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.
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Add to basketCondition: New. PRINT ON DEMAND.