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A feature extraction technique for online handwriting recognition

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : A feature extraction technique for online handwriting recognition
นักวิจัย : Verma, Brijesh. , Lu, Jenny. , Ghosh, Moumita. , Ghosh, Ranadhir.
คำค้น : Penmanship. , 700103 Information processing services , 280205 Text Processing , TBA. , 890205 Information Processing Services (incl. Data Entry and Capture) , 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. , Writing. , Optical pattern recognition. , Neural networks (Computer science)
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2547
อ้างอิง : http://hdl.cqu.edu.au/10018/4322 , cqu:2052
ที่มา : Verma, B, Lu, J, Ghosh, M & Ghosh, R 2004, 'A feature extraction technique for online handwriting recognition', paper presented at 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)., Budapest, Hungary.
ความเชี่ยวชาญ : -
ความสัมพันธ์ : 2004 Joint International Neural Networks Conference (IJCNN), and of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway , NJ, USA. : Institute of Electrical and Electronics Engineers Inc., 2004. p. 1337-1341 5 pages Refereed 0780383545 , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The paper presents a feature extraction technique for online handwriting recognition. The technique incorporates many characteristics of handwritten characters based on structural, directional and zoning information and combines them to create a single global feature vector. The technique is independent to character size and it can extract features from the raw data without resizing. Using the proposed technique and a Neural Network based classifier, many experiments were conducted on UNIPEN benchmark database. The recognition rates are 98.2% for digits, 91.2% for uppercase and 91.4% for lowercase.

บรรณานุกรม :
Verma, Brijesh. , Lu, Jenny. , Ghosh, Moumita. , Ghosh, Ranadhir. . (2547). A feature extraction technique for online handwriting recognition.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Lu, Jenny. , Ghosh, Moumita. , Ghosh, Ranadhir. . 2547. "A feature extraction technique for online handwriting recognition".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Verma, Brijesh. , Lu, Jenny. , Ghosh, Moumita. , Ghosh, Ranadhir. . "A feature extraction technique for online handwriting recognition."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2547. Print.
Verma, Brijesh. , Lu, Jenny. , Ghosh, Moumita. , Ghosh, Ranadhir. . A feature extraction technique for online handwriting recognition. กรุงเทพมหานคร : Central Queensland University, Australia; 2547.