Error Reduction Based on Error Categorization in Arabic Handwritten Numeral Recognition

In practical applications, errors should not be treated equally, but conditionally. In this paper, errors are categorized based on different costs in misclassification. Accordingly, the characteristics of the error categorization and the corresponding strategies for correcting them are proposed. Verification based on Arabic Handwritten Numeral Recognition is considered as one application to utilize these definitions and strategies. As a result, the recognition results improved from 98.47% to 99.05%, and errors were significantly reduced by over 35% compared to previous studies. When a rejection measurement was applied, and the rejection threshold was adjusted to maintain the same error rate, both the recognition rate and reliability increased from 96.98% to 97.89% and from 99.08% to 99.28%, respectively.