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A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier
นักวิจัย : Ghosh, Moumita. , Ghosh, Ranadhir. , Verma, Brijesh.
คำค้น : 700101 Application packages. , 280205 Text Processing. , 890203 Computer Gaming Software. , 8902 Computer Software and Services. , 89 Information and Communication Services. , 080107 Natural Language Processing. , 0801 Artificial Intelligence and Image Processing. , 08 Information and Computing Sciences. , Pattern recognition systems. , Neural networks (Computer science) , Writing , Handwriting recognition -- Offline -- Segmentation -- Hybrid learning
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2547
อ้างอิง : http://hdl.cqu.edu.au/10018/21671
ที่มา : Ghosh, R, Ghosh, M & Verma, B 2004, 'A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier', International Journal of Pattern Recognition and Artificial Intelligence., vol. 18, no. 7, pp. 1267-1284.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : International journal of pattern recognition and artificial intelligence. Singapore. : World Scientific, 2004. Vol. 18, no. 7 (2004), p. 1267-1284 18 pages Refereed 0218-0014 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

บรรณานุกรม :
Ghosh, Moumita. , Ghosh, Ranadhir. , Verma, Brijesh. . (2547). A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Ghosh, Moumita. , Ghosh, Ranadhir. , Verma, Brijesh. . 2547. "A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Ghosh, Moumita. , Ghosh, Ranadhir. , Verma, Brijesh. . "A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2547. Print.
Ghosh, Moumita. , Ghosh, Ranadhir. , Verma, Brijesh. . A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier. กรุงเทพมหานคร : Central Queensland University, Australia; 2547.