An On-line Handwritten Text Search Method Based on Directional Feature Matching

In this paper, we describe a method of retrieving on-line handwritten text based on directional feature matching. Although text search into the character recognition candidate lattice has been elaborated, the character recognition based approach does not support languages which are not assumed. The proposed method is liberated from this constraint. It first hypothetically segments on-line handwritten text into character pattern blocks and prepares the object text patterns by combining the character pattern blocks. On the other hand, it employs handwritten text as a query pattern or prepares a query pattern by combining character ink patterns from query character codes. Then, it extracts directional features from the object text patterns and the query pattern, and the dimensionalities of those features are further reduced by Fisher linear discriminate analysis (FDA). Finally, the similarity is measured between the object text patterns and the query pattern by block-shift matching. This paper discusses the retrieval performance in comparison with our previous character recognition based method.