Handwritten Character Recognition Research Based on Adaptive Minimum Distance Classifiers Integration
It is difficulty to gain completely satisfactory effect if a single classification is used to check a complicated recognition classification problem. Using the complementarities between the different classify method, integrating many classifiers, it can reduce the identification mistake and strengthen recognition robustness. Taking a offline handwritten number recognition system as an example, adopting Bayesian discriminate function based on minimal mistake rate, uniting recognition algorithm of RBF kernel function, using Bagging technology, adaptive minimum distance classifiers integration is designed in which there is minimal mistake rate. Furthermore, an offline handwritten number recognition system in high accuracy is exploited in which there is adaptive and self-learning function. It can be used for important economic fields such as financial statement and bank paper.
Z. P. Tang et al., "Handwritten Character Recognition Research Based on Adaptive Minimum Distance Classifiers Integration", Applied Mechanics and Materials, Vols. 44-47, pp. 3388-3392, 2011