A Model-Based Ruling Line Detection Algorithm for Noisy Handwritten Documents

Ruling lines are commonly used to help people write neatly on paper. In document image analysis, however, they create challenges for handwriting recognition and writer identification. In this paper, we model ruling line detection as a multi-line linear regression problem and then derive a globally optimal solution giving the Least Square Error. We demonstrate the efficacy of the technique on both synthetic and real datasets. A comparative study shows that our algorithm outperforms a previously published method on the public Germana dataset.