• The Transactions of the Korea Information Processing Society
• /
• v.5 no.10
• /
• pp.2600-2608
• /
• 1998

This paper proposes an algorithm for off line recognition of handwritten characters, especially effective for large-set characters such as Korean and Chinese characters. The algorithm is based on a minimum distance dlassification method which is simple and easy to implement but suffers from low recognition performance. Two strategies have been developed to improve its performance; one is multi-stage pre-classification and the other is candicate reordering. Effectiveness of the algorithm has been proven by and experimet with the samples of 574 classes in a handwritten Korean character catabase named PE02, where 86.0% of recognition accuracy and 15 characters per second of processing speed have been obtained.

• The Journal of the Korea Contents Association
• /
• v.9 no.3
• /
• pp.305-311
• /
• 2009

The bankruptcy in Korea affects to all stakeholder of firms. Companies listed in KOSDAQ have high technology but the possibilities for success of business are low. The purpose of this study is to develop and to applicate falling prediction model of KOSDAQ firms using logistic regression analysis. The results of this study are as follows. First, the accuracy of classification of the models by years was between 76.5% and 77.5%, and that of the mean model was between 70.6% and 83.4%. Among the models, the mean model of -three years, -two years, and -one year was highest in the accuracy of classification (83.4%). Second, when the mean model of -three year, -two years, and -one years, the highest model in accuracy of classification, was selected to be verified on validation samples, the accuracy of prediction increased from -three years to -one year (71.7% for -three years, 75.0% for -two years, 90.0% for -one year). In indicating the superiority of developed model.