| 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.
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