In this paper, we present a connected-component (CC)-based text extraction method for automatic detection and segmentation of text from the digital images. This technique can be applied to computer vision, sign extraction and recognition. We use a fast algorithm for labeling connected component to generated CCs. Then, by combining the simple geometry feature to filter majority of the CCs, the wavelet features and texture features are extracted from the remaining CCs and sent to the Adaboost classifier as an input for classification judgment. With this strong classifiers, the CCs can be easily categorized either as texts or non-text characters, thus when CC session pass through the strong classifiers, it can categorize CC as text and treated it as the final extraction result. The present research integrated the wavelet features and texture features, which sucessfully facilitated the precision rates of text extraction up to 94.65 %. Furthermore, the computational cost be efficiently reduced through the speed of convergence of Adaboost algorithm.
