Word Image Matching Using Dynamic Time Warping.

Libraries and other institutions are interested in provid- ing access to scanned versions of their large collections of handwritten historical manuscripts on electronic media. Convenient access to a collection requires an index, which is manually created at great labour and expense. Since cur- rent handwriting recognizers do not perform well on histor- ical documents, a technique called word spotting has been developed: clusters with occurrences of the same word in a collection are established using image matching. By an- notating "interesting" clusters, an index can be built auto- matically. We present an algorithm for matching handwritten words in noisy historical documents. The segmented word images are preprocessed to create sets of 1-dimensional features, which are then compared using dynamic time warping. We present experimental results on two different data sets from the George Washington collection. Our experiments show that this algorithm performs better and is faster than com- peting matching techniques.