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- W2049046330 abstract "Recognition rate is traditionally used as the main criterion for evaluating the performance of a recognition system. High recognition reliability with low misclassification rate is also a must for many applications. To handle the variability of the writing style of different individuals, this paper employs decision trees and WRB AdaBoost to design a classifier with high recognition reliability for recognizing Bangla handwritten numerals. Experiments on the numeral images obtained from real Bangladesh envelopes show that the proposed recognition method is capable of achieving high recognition reliability with acceptable recognition rate." @default.
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- W2049046330 date "2008-01-27" @default.
- W2049046330 modified "2023-09-27" @default.
- W2049046330 title "<title>Achieving high recognition reliability using decision trees and AdaBoost</title>" @default.
- W2049046330 doi "https://doi.org/10.1117/12.766059" @default.
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