Abstract Searching and indexing historical handwritten collections are a very challenging problem. We describe an approach called word spotting which involves grouping word images into clusters of similar words by using image matching to find similarity. By annotating "interesting" clusters, an index that links words to the locations where they occur can be built automatically. Image similarities computed using a number of different techniques including dynamic time warping are compared. The word similarities are then used for clustering using both K-means and agglomerative clustering techniques. It is shown in a subset of the George Washington collection that such a word spotting technique can outperform a Hidden Markov Model wordbased recognition technique in terms of word error rates.
Rath & Manmatha use word spotting to index the George Washington manuscripts REF .
Word spotting for historical documents
International Journal of Document Analysis and Recognition (IJDAR)
International Journal of Document Analysis and Recognition (IJDAR)
Computer Science