Abstract
We introduce a novel approach for text line detection in document images, keeping in mind the requirements of a portable text recognition system designed to support the blind. Challenges include shadows, cluttered backgrounds, and perspective distortion. Different from previous approaches, the proposed method does not segment the image. A text model is created by clustering SIFT features extracted from positive and negative examples. Text regions are located by matching the features extracted from the input image to the clusters in the text model. Regions around the correspondences are then analyzed, and text lines are identified based on features such as gradients and histogram distribution. Experimental results show that our approach outperforms a state-of-the-art text detector in a text/non-text classification task.
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