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Text detection and segmentation in complex color images

Text is a very powerful index in content-based image and video indexing. We propose a new text detection and segmentation algorithm that is especially designed for being applied to color images with complicated background. Our goal is to minimize the number of false alarms and to binarize efficiently the detected text areas so that they can be processed by standard OCR software. First, potential areas of text are detected by enhancement and clustering processes, considering most of constraints related to the texture of words. Then, classification and binarization of potential text areas are achieved in a single scheme performing color quantization and characters periodicity analysis. We report a high rate of good detection results with very few false alarms and reliable text binarization.