16. A method of detecting text in real-world images comprising: dividing an image representing a real-world scene into one or more regions; calculating a cascade of classifiers, the cascade comprising a plurality of stages, each stage including one or more weak classifiers, the plurality of stages organized to start out with classifiers that are most useful for ruling out non-text regions of the image; receiving training images; feeding the training images into the cascade; comparing classifier results to known training image results; and adapting one or more of an order of stages in the cascade, an order of classifiers in the stages, one or more classifier confidence level thresholds, and the classifiers by selecting features for each classifier that reduce a number of false positive and false negative detections by a reduced number of tests; feeding the one or more regions into the cascade; and removing regions of the image classified as the non-text regions from the cascade prior to completion of the cascade to avoid subsequent processing of the removed regions; and displaying a result.
19. The method of claim 16 , further comprising grouping the connected components into lines of text based on a color distance between colors of two connected components.