Machine Learning Script Recognition: Machine Vision
6 Angebote vergleichen
| Preise | 2013 | 2014 | 2015 | 2019 |
|---|---|---|---|---|
| Schnitt | € 68,00 | € 77,07 | € 82,16 | € 95,72 |
| Nachfrage |
1
Symbolbild
Machine Learning and Script Recognition
DE PB NWISBN: 9783659111709 bzw. 3659111708, in Deutsch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Von Händler/Antiquariat, BuySomeBooks [52360437], Las Vegas, NV, U.S.A.
Paperback. 168 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
Paperback. 168 pages. Dimensions: 8.7in. x 5.9in. x 0.4in.Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN.
2
Symbolbild
Machine Learning Script Recognition (2015)
DE PB NWISBN: 9783659111709 bzw. 3659111708, in Deutsch, LAP LAMBERT ACADEMIC PUB 01/01/2015, Taschenbuch, neu.
Von Händler/Antiquariat, Books2Anywhere [190245], Fairford, GLO, United Kingdom.
New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. This item is printed on demand.
New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. This item is printed on demand.
3
Symbolbild
Machine Learning and Script Recognition: Machine Vision
DE PB NWISBN: 9783659111709 bzw. 3659111708, in Deutsch, Taschenbuch, neu.
Von Händler/Antiquariat, BuySomeBooks [52360437], Las Vegas, NV, U.S.A.
This item is printed on demand. Paperback. Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations. This item ships from La Vergne,TN.
This item is printed on demand. Paperback. Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations. This item ships from La Vergne,TN.
4
Machine Learning and Script Recognition (2013)
~EN PB NWISBN: 9783659111709 bzw. 3659111708, vermutlich in Englisch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Schweiz, Versandfertig innert 4 - 7 Werktagen.
Machine Vision, Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations. Taschenbuch, 26.03.2013.
Machine Vision, Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations. Taschenbuch, 26.03.2013.
5
Machine Learning and Script Recognition
DE PB NWISBN: 9783659111709 bzw. 3659111708, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
buecher.de GmbH & Co. KG, [1].
Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations.Versandfertig in 3-5 Tagen, Softcover.
buecher.de GmbH & Co. KG, [1].
Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations.Versandfertig in 3-5 Tagen, Softcover.
6
Machine Learning and Script Recognition
DE PB NWISBN: 9783659111709 bzw. 3659111708, in Deutsch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
buecher.de GmbH & Co. KG, [1].
Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations.Versandfertig in 3-5 Tagen, Softcover.
buecher.de GmbH & Co. KG, [1].
Machine simulation to recognize handwriting has opened new horizons to improve human-computer interface and perform repetitive task of reading by machines. Despite nearly four decades of research, offline unconstrained cursive script segmentation and recognition remains an open problem. Currently, accuracy of offline cursive script recognition schemes is low particularly for touched script in data entry forms and computationally expensive for real world applications. This book presents latest machine learning techniques for script recognition from pre-processing to post-processing. Character segmentation is a focus of this book as poor segmentation significantly effects accuracy. Several interesting experiments are carried out and results are compared with existing pre-processing and post-processing techniques reported in the state of art using benchmark database such as NIST, IAM and CEDAR. Finally, latest techniques are integrated into a model script recognition system. Remaining problems are also highlighted along with suggestions and recommendations.Versandfertig in 3-5 Tagen, Softcover.
Lade…