Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex *** approaches like genetic algorithms have been used in the past for various optimization as...
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Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex *** approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks,reporting exceptional *** proposed Urdu ligature recognition system uses a genetic algorithm for optimization and *** the proposed recognition system observes the processes of pre-processing,segmentation,feature extraction,hierarchicalclustering,classification rules and genetic algorithm optimization and *** pre-processing stage removes noise from the sentence images,whereas,in segmentation,the sentences are segmented into ligature *** features are extracted from each of the segmented ligature ***-featurehierarchicalclustering is observed that results in clustered ***,classification rules are used for the representation of the clustered *** genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu *** conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results,achieving a recognition rate of 96.72%.
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