Off-line isolated handwritten Thai OCR using island-based projection with n-gram model and hidden Markov models |
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| Institution: | 1. Information Technology Program, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani 12121, Thailand;2. Department of Computer Science, Faculty of Science, Payap University, Chiangmai, Thailand;1. Department of Forestry, National Chung Hsing University, Taichung 402, Taiwan;2. Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu 300, Taiwan;1. Nicholas Institute for Environmental Policy Solutions, Duke University, Box 90335, Durham, NC 27708, USA;2. College of Natural Resources, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA;3. College of Forestry, Department of Forest Engineering, Resources & Management, Oregon State University, Corvallis, OR 97331, USA |
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| Abstract: | Many traditional works on off-line Thai handwritten character recognition used a set of local features including circles, concavity, endpoints and lines to recognize hand-printed characters. However, in natural handwriting, these local features are often missing due to rough or quick writing, resulting in dramatic reduction of recognition accuracy. Instead of using such local features, this paper presents a method called multi-directional island-based projection to extract global features from handwritten characters. As the recognition model, two statistical approaches, namely interpolated n-gram model (n-gram) and hidden Markov model (HMM), are proposed. The experimental results indicate that the proposed scheme achieves high accuracy in the recognition of naturally-written Thai characters with numerous variations, compared to some common previous feature extraction techniques. Another experiment with English characters also displays quite promising results. |
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