Grouping Text Lines in Online Handwritten Japanese Documents by Combining Temporal and Spatial Information

We present an effective approach for grouping text lines in online handwritten Japanese documents by combining temporal and spatial information. Initially, strokes are grouped into text line strings according to off-stroke distances. Each text line string is segmented into text lines by dynamic programming (DP) optimizing a cost function trained by the minimum classification error (MCE) method. Over-segmented text lines are then merged with a support vector machine (SVM) classifier for making merge/non-merge decisions, and last, a spatial merge module corrects the segmentation errors caused by delayed strokes. In experiments on the TUAT Kondate database, the proposed approach achieves the Entity Detection Metric (EDM) rate of 0.8816, the Edit-Distance Rate (EDR) of 0.1234, which demonstrates the superiority of our approach.