This paper compares several information retrieval (IR) methods applied to the problem of retrieving specific words from a handwritten document. The methods compared include variants of the Okapi formula and Latent Semantic Indexing (LSI); recognition-based retrieval; and keyword search. One novel aspect of the work presented is that it uses the output stack of a Hidden Markov Model (HMM) handwriting recognizer with a 30,000-word lexicon to convert each handwritten word into a document which is then used for document retrieval. Preliminary experiments on a database of 1158 words from 75 writers indicate that the keyword search has superior precision and recall for text queries, and that ink queries result in minor performance reductions.
CITATION STYLE
Kwok, T., Perrone, M. P., & Russell, G. (2000). Ink retrieval from handwritten documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1983, pp. 461–466). Springer Verlag. https://doi.org/10.1007/3-540-44491-2_67
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