An efficient classification approach based on grid code transformation and mask-matching method

In this paper, we present a two-stage classification approach to recognize the characters in the rare books transcribed by ancient calligraphers. The first stage is coarse classification which uses grid code transformation (GCT) method to quantize the most significant discrete cosine transform coefficients into a finite number of grids. On classifying an unknown character, a reduced set of candidate classes can be retrieved from the corresponding grid code. The second stage is fine classification, which uses a statistical mask-matching method to identify the individual target in the set given by the first stage. In the training phase, we generate one positive mask and one negative mask for each distinct class of characters. Therefore, an unknown character can be recognized by finding the prototype character whose masks are best fitted to it. Experiments were conducted for recognizing handwritten characters in Chinese paleography and showed that our approach performs well in this application domain.