Comparison of MRF and CRF for Text/Non-text Classification in Japanese Ink Documents

Abstract

In the paper, we compare the methods based on Markov Random fields (MRF) and Conditional Random fields (CRF) for separating text and non-text ink strokes in online handwritten Japanese documents. This paper validates the effect of context information in neighbor strokes based on graphical models of MRF and CRF. The task of separating text and non-text ink strokes in ink documents denotes classifying ink strokes into two classes (text and non-text). For classification, Support Vector Machine (SVM) classifiers are trained on the set of ink strokes. After converting the SVM's outputs to likelihood probabilities, they are assigned to the likelihood clique potentials of MRF and the feature functions of CRF. The classification based on MRF or CRF is considered as a labeling problem, which can be solved using a labeling algorithm. The experiments on Japanese ink documents in the Kondate database shows that the proposed method based on CRF achieves a classification rate of 98.02% while the method based on MRF produces the classification rate of 97.86%.