2. The method of claim 1 , wherein the cascade comprises seven AdaBoost layers.
1. 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; 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; utilizing a binarization process including classifying individual pixels as one of: non-text, light potential-text, and dark potential-text based on one or more factors including: a number of pixels in the connected component; a number of pixels on the border of the connected component; a height of the connected component; a width of the connected component; a ratio of the height of the connected component to the width of the connected component; a ratio of the pixels in the connected component to the width of the connected component multiplied by the height of the connected component; a local size of text in the connected component; outputting binarization output data.