Abstract

An automatic text recognizer needs, in first place, to localize the text in the image the more accurately possible. For this purpose, we present in this paper a robust method for text detection. It is composed of three main stages: a segmentation stage to find charac-ter candidates, a connected component analysis based on fast-to-compute but robust features to accept charac-ters and discard non-text objects, and finally a text line classifier based on gradient features and support vector machines. Experimental results obtained with several challenging datasets show the good performance of the proposed method, which has been demonstrated to be more robust than using multi-scale computation or sli-ding windows. 1.