Morphology-based Text Line Extraction

Abstract

This paper presents a morphology-based text line extraction algorithm for extracting text regions from cluttered images. First of all, the method defines a novel set of morphological operations for extracting important contrast regions as possible text line candidates. The contrast feature is robust to lighting changes and invariant against different image transformations like image scaling, translation, and skewing. In order to detect skewed text lines, a moment-based method is then used for estimating their orientations. According to the orientation, an x-projection technique can be applied to extract various text geometries from the text-analogue segments for text verification. However, due to noise, a text line region is often fragmented to different pieces of segments. Therefore, after the projection, a novel recovery algorithm is then proposed for recovering a complete text line from its pieces of segments. After that, a verification scheme is then proposed for verifying all extracted potential text lines according to their text geometries. Thus, different desired text lines can be well and correctly detected from images or video frames for various document analyses. Different from traditional training methods which use an exhaustingly searching way to find all possible skewed text lines, the proposed technique can locate text lines very efficiently no matter what orientations they have. The average accuracy of the proposed system is 95.4%. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness for text line detection