Ink retrieval from handwritten documents

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Abstract

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.

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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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