The impact of discretization methods on Chinese handwriting identification

Abstract

Identification based on Chinese handwriting is an interesting research in the field of pattern recognition and computer vision. Recently, many innovative methods and approaches have been developed for writer identification. Unlike character of western alphabet such as English, German, French, some oriental character such as Korean, Arabic and Chinese have structural characteristics. These structural characteristics, particularly on Chinese character have a complex structure due to the numerous strokes that warped into a cursive shape and have much larger set of characters. Hence, more features are needed to be generated prior to the classification phase for better identification. However, these features need to be well-represented for identification purposes. Hence in this study, an improved discretization is implemented to transform the range of continuous quantitative values of writer’s features into a number of appropriate intervals, denoted as an integer label. Several experiments have been conducted with two different types of datasets: pre-discretized and post-discretized datasets. Post-discretized datasets is the extarcted features that have performed with discretization process; while pre-discretized are the original features, obtained from Direction-based Feature Extraction (DFE) technique. For reliable identification performance through discretization, 10, 7 and 5 crossvalidations (CV) have been tested on both datasets. The experiments have shown that the overall best result are obtained with discretized data, with identification accuracy above 94.0% compared to pre-discretized with identification accuracy below 50.0%. It can be concluded that the discretization process is efficient for representing the writers’ features in obtaining higher identification rates for better forensic document analysis.