Последнее обновление: 25.10.2006   Статьи / Offline в Online

Offline в Online

A FAST LEXICALLY CONSTRAINED VITERBI ALGORITHM FOR ONLINE HANDWRITING RECOGNITION Авторы: ALAIN LIFCHITZ, FREDERIC MAIRE
Организация: Laboratoire d'Informatique de Paris 6, Universite P6 & CNRS, Paris School of Computing Science, Queensland University of Technology, Australia
Дата: 2000 год
Кол-во страниц: 10
Most on-line cursive handwriting recognition systems use a lexical constraint to help improve the recognition performance. Traditionally, the vocabulary lexicon is stored in a trie (automaton whose underlying graph is a tree). In this paper, we propose a solution based on a more compact data structure, the directed acyclic word graph (DAWG). We show that our solution is equivalent to the traditional system. Moreover, we propose a number of heuristics to reduce the size of the DAWG and present experimental results demonstrating a significant improvement.
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FROM OFF-LINE TO ON-LINE HANDWRITING RECOGNITION Авторы: P.M. LALLICAN , C. VIARD-GAUDIN , S. KNERR
Организация: Vision Objects, France Ecole Polytechnique de l’Universite de Nantes, France
Дата: 2000 год
Кол-во страниц: 10
On-line handwriting includes more information on the time order of the writing signal and on the dynamics of the writing process than off-line handwriting. Therefore, on-line recognition systems achieve higher recognition rates. This can be concluded from results reported in the literature, and has been demonstrated empirically as part of this work. We propose a new approach for recovering the time order of the off-line writing signal. Starting from an over-segmentation of the off-line handwriting into regular and singular parts, the time ordering of these parts and recognition of the word are performed simultaneously. This approach, termed “OrdRec”, is based on a graph description of the handwriting signal and a recognition process using Hidden Markov Models (HMM). A complete omni-scriptor isolated word recognition system has been developed. Using a dynamic lexicon and models for upper and lower case characters, our system can process binary and gray value word images of any writing style (script, cursive, or mixed). Using a dual handwriting data base which features both the on-line and the off-line signal for each of the 30 000 words written by about 700 scriptors, we have shown experimentally that such an off-line recognition system, using the recovered time order information, can achieve recognition performances close to those of an on-line recognition system.
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