MULTIPLE FEATURE INTEGRATION FOR WRITER VERIFICATION

Given two handwritten documents, the writer verification problem is to determine whether the two documents were written by the same person. It is tackled by extracting various features and classi­ fying the patterns into their classes. Features are diverse in type while techniques in pattern recognition typically require that features be ho­ mogeneous. The solution proposed overcomes both the non­homogeneity of features and the intractability of infinite number of writers by a di­ chotomy transformation. In this model, the distance between each homogeneous feature type is used. We integrate several distance measures for many feature types: element, histogram, string, convex hull, etc into one useful for writer verification. Experimental results with 1; 000 writers with three sample documents per writer, using only 12 feature distances, results in 97% accuracy.