Analyzing the distribution of a large-scale character pattern set using relative neighborhood graph

The goal of this research is to understand the true distribution of character patterns. Advances in computer technology for mass storage and digital processing have paved way to process a massive dataset for various pattern recognition problems. If we can represent and analyze the distribution of a large-scale character pattern set directly and understand its relationships deeply, it should be helpful for improving character recognizer. For this purpose, we propose a network analysis method to represent the distribution of patterns using a relative neighborhood graph and its clustered version. In this paper, the properties and validity of the proposed method are confirmed on 410,564 machine-printed digit patterns and 622,660 handwritten digit patterns which were manually ground-truthed and resized to 16 times 16 pixels. Our network analysis method represents the distribution of the patterns without any assumption, approximation or loss.