Automatic character detection in video sequences is a complex task, due to the variety of sizes and colors as well as to the complexity of the background. In this paper we address this problem by proposing a localization/verification scheme. Candidate text regions are first localized by using a fast algorithm with a very low rejection rate, which enables the character size normalization. Contrast independent features are then proposed for training machine learning tools in order to verify the text regions. Two kinds of machine learning tools, multilayer perceptrons and support vector machines, are compared based on four different features in the verification task. This scheme provides fast text detection in images and videos with a low computation cost, comparing with traditional methods.