- Title
- A feature extraction technique for online handwriting recognition
- Creator
- Verma, Brijesh; Lu, Jenny; Ghosh, Moumita; Ghosh, Ranadhir
- Date
- 2004
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/55558
- Identifier
- vital:1624
- Abstract
- 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.; E1
- Publisher
- Budapest, Hungary : IEEE
- Relation
- Paper presented at 2004 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary : 25th July, 2004
- Rights
- Copyright IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Handwriting; Text recognition
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