The Handwritten Trie: Indexing Electronic Ink.

Walid G. Aref, Daniel Barbará, Padmavathi Vallabhaneni: The Handwritten Trie: Indexing Electronic Ink. SIGMOD Conference 1995: 151-162

@inproceedings{DBLP:conf/sigmod/ArefBV95,
  author    = {Walid G. Aref and
               Daniel Barbar{\'a} and
               Padmavathi Vallabhaneni},
  editor    = {Michael J. Carey and
               Donovan A. Schneider},
  title     = {The Handwritten Trie: Indexing Electronic Ink},
  booktitle = {Proceedings of the 1995 ACM SIGMOD International Conference on
               Management of Data, San Jose, California, May 22-25, 1995},
  publisher = {ACM Press},
  year      = {1995},
  pages     = {151-162},
  ee        = {http://doi.acm.org/10.1145/223784.223811, db/conf/sigmod/sigmod95-11.html},
  crossref  = {DBLP:conf/sigmod/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

The emergence of the pen as the main interface device for personal digital assistants and pen-computers has made handwritten text, and more generally ink, a first-class object. As for any other type of data, the need of retrieval is a prevailing one. Retrieval of handwritten text is more difficult than that of conventional data since it is necessary to identify a handwritten word given slightly different variations in its shape. The current way of addressing this is by using handwriting recognition, which is prone to errors and limits the expressiveness of ink. Alternatively, one can retrieve from the database handwritten words that are similar to a query handwritten word using techniques borrowed from pattern and speech recognition. In particular, Hidden Markov Models (HMM) can be used as representatives of the handwritten words in the database. However, using HMM techniques to match the input against every item in the database (sequential searching) is unacceptably slow and does not scale up for large ink databases. In this paper, an indexing technique based on HMMs is proposed. The new index is a variation of the trie data structure that uses HMMs and a new search algorithm to provide approximate matching. Each node in the tree contains handwritten letters, where each letter is represented by an HMM. Branching in the trie is based on the ranking of matches given by the HMMs. The new search algorithm is parametrized so that it provides means for controlling the matching quality of the search process via a time-based budget. The index dramatically improves the search time in a database of handwritten words. Due to the variety of platforms for which this work is aimed, ranging from personal digital assistants to desktop computers, we implemented both main-memory and disk-based systems. The implementations are reported in this paper, along with performance results that show the practicality of the technique under a variety of conditions.

Copyright © 1995 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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