Similarity Evaluation and Shape Feature Extraction for Character Pattern Retrieval to Support Reading Historical Documents

We have many historical documents written in over 1,000 years ago. Shape features of character patterns on the documents are unstable or missing because most of the documents have been stained and degraded deeply. Digital archives of the documents with accurate character pattern retrieval methods are helpful for archaeologists and historians. In this paper, we propose a similarity evaluation method for character patterns with missing shape parts. It collaboratively works with non-linear normalization for such patterns, and modifies the templates for each trial of the retrieval efficiently. In the experiences using 4,911 Kanji (Chinese origin) character patterns from the Japanese historical documents called mokkans, the method shows improvements of the retrieval accuracy. Also, we present a simple implementation of gradient feature extraction to compare the chain code feature with the gradient feature in the retrieval. As the result, the gradient feature works better than the chain code feature.