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This paper proposes a novel method to identify data patterns that mutually occlude each other. In pattern recognition problems, occlusion is a challenge which severely limits the accuracy of recognition. The technique makes use of an evolutionary algorithm at a micro level by extracting a population from the occluded text pattern and by doing the operations such as selection, crossover, mutation and reinsertion on the members in the population. The hidden text is found out after certain number of generations.The technique works well even if the occlusion is mutual, that is, when both text patterns occlude each other and even if up to 90% of the pattern is hidden.