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Text information extraction from camera captured text embedded images has a wide variety of applications. In this paper, a fuzzy membership based robust text detection technique is presented. The given image is partitioned into blocks that are assigned two types of fuzyy memberships. The membership values are post-processed for finer classification as foreground block or background block. Adjacent foreground blocks form foreground components. Then, a feature-based Multi Layer Perceptron is used to classify the foreground components as text or non-text. Experiments show that the number of false negative is very small compared to that of the false positives. The technique yields an average of 99.75% recall and 93.75% precision rates.