Hybrid Feature Selection for Historical Document Layout Analysis

In this paper we propose a novel hybrid feature selection method for historical Document Image Analysis (DIA). Adapted greedy forward selection and genetic selection are used in a cascading way. We apply the proposed method to the task of historical document layout analysis on three handwritten datasets of diverse nature. The documents contain complex layouts, different handwriting styles, and several results of decay. The task is to segment each page into four areas: periphery, background, text block, and decoration. The proposed method selected significantly less features and resulted in significantly lower error rates than using all features. Compared to several conventional feature selection methods, the proposed method is competitive with respect to the number of selected features and the resultant error rates. In addition, we found that some features, e.g., Gradient, Laplacian, and local binary patterns (LBP), are selected by most of the feature selection methods and we give some explanations. This finding suggests a clue for the layout analysis on handwritten documents in general.