Fast retrieval of cursive handwriting
Ibrahim Kamel · 1996
This paper presents an indexing method that can be used to search a large collection of cursive handwriting. The basic idea is to segment each cursive string into a set of strokes. Each of these strokes can be described with a set of features and, thus, can be stored as points in the feature space. The Karhuraen-Lor%e transform is then used to minimize the number of features used (data dimensionality) and thus the index size. Feature vectors are stored in an R-tree. Similarity search can be performed by executing a few range queries and then applying a simple voting algorithm to the output to select the strings that are most similar to the query. The proposed index can support similarity queries as well as substring mat thing. It is resilient to the kind of errors that result from the segmentation process, namely, stroke insertion/deletion and m-n substitution.