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Ensemble classifier composition : impact on feature based offline cursive character recognition

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Ensemble classifier composition : impact on feature based offline cursive character recognition
นักวิจัย : Rahman, Ashfaqur. , Verma, Brijesh.
คำค้น : Pattern recognition systems. , Applied research. , 890202 Application Tools and System Utilities. , 080108 Neural, Evolutionary and Fuzzy Computation. , 080109 Pattern Recognition and Data Mining. , Writing , Optical character recognition. , Neural networks (Computer science) , Ensemble neural classifier -- Feature extraction -- Handwriting recognition
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2554
อ้างอิง : http://hdl.cqu.edu.au/10018/918071
ที่มา : Rahman, A & Verma, B 2011, 'Ensemble classifier composition: impact on feature based offline cursive character recognition', paper presented to the 2011 International Joint Conference on Neural Networks (IJCNN), 31 July - 5 August, San Jose, CA., http://dx.doi.org/10.1109/IJCNN.2011.6033303
ความเชี่ยวชาญ : -
ความสัมพันธ์ : Proceedings of International Joint Conference on Neural Networks (IJCNN), San Jose, CA, 31 July-5 August 2011. USA : IEEE, 2011. p. 801-807 7 pages Refereed 9781424496358 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

In this paper we propose different ensemble classifier compositions and investigate their influence on offline cursive character recognition. Cursive characters are difficult to recognize due to different handwriting styles of different writers. The recognition accuracy can be improved by training an ensemble of classifiers on multiple feature sets focussing on different aspects of character images. Given the feature sets and base classifiers, we have developed multiple ensemble classifier compositions using three architectures. Type-1 architecture is based on homogeneous base classifiers and Type-2 architecture is composed of heterogeneous base classifiers. Type-3 architecture is based on hierarchical fusion of decisions. The experimental results demonstrate that the presented method with best composition of classifiers and feature sets performs better than existing methods for offline cursive character recognition.

บรรณานุกรม :
Rahman, Ashfaqur. , Verma, Brijesh. . (2554). Ensemble classifier composition : impact on feature based offline cursive character recognition.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Rahman, Ashfaqur. , Verma, Brijesh. . 2554. "Ensemble classifier composition : impact on feature based offline cursive character recognition".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Rahman, Ashfaqur. , Verma, Brijesh. . "Ensemble classifier composition : impact on feature based offline cursive character recognition."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2554. Print.
Rahman, Ashfaqur. , Verma, Brijesh. . Ensemble classifier composition : impact on feature based offline cursive character recognition. กรุงเทพมหานคร : Central Queensland University, Australia; 2554.