A Model-based Line Detection Algorithm in Documents

In this paper we present a novel model based approach to detect severely broken parallel lines in noisy textual documents. It is important to detect and remove these lines so the text can be segmented and recognized. We use Directional Single-Connected Chain, a vectorization based algorithm, to extract the line segments. We then instantiate a parallel line model with three parameters: the skew angle, the vertical line gap, and the vertical translation. A coarse-to-fine approach is used to improve the estimation accuracy. From the model we can incorporate the high level contextual information to enhance detection results even when lines are severely broken. Our experimental results show our method can detect 94% of the lines in our database with 168 noisy Arabic document images.