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<Paper uid="H94-1086">
  <Title>On-Line Cursive Handwriting Recognition Using Hidden Markov Models and Statistical Grammars</Title>
  <Section position="3" start_page="0" end_page="432" type="intro">
    <SectionTitle>
2. COMPARISON OF CONTINUOUS
SPEECH RECOGNITION TO ON-LINE
HANDWRITING RECOGNITION
</SectionTitle>
    <Paragraph position="0"> On-line handwriting and continuous speech share many common characteristics. On-line handwriting can be viewed as a signal (x,y coordinates) over time, just like in speech. The items to be recognized are well-defined (usually the alphanumeric characters) and finite in number, as are the phonemes in speech. The shape of a handwritten character depends on its neighbors. Correspondingly, spoken phonemes change due to coarticulation in speech. In both cases, these basic units form words and the words form phrases.</Paragraph>
    <Paragraph position="1"> Thus, language modeling can be applied to improve recognition performance for both problems.</Paragraph>
    <Paragraph position="2"> In spite of the above similarities, handwriting recognition has some basic differences to speech recognition. Unlike continuous speech, word boundaries are usually distinct in handwriting. Thus, words should be easier to distinguish. However, in cursive writing the dots and crosses involved in the characters &amp;quot;i&amp;quot;, &amp;quot;j&amp;quot;, &amp;quot;x&amp;quot;, and &amp;quot;t&amp;quot; are not added until after the whole word is written. Thus, all the evidence for a character may not be contiguous. Additionally, in words with multiple crossings (&amp;quot;t&amp;quot; and &amp;quot;x&amp;quot;) and/or dottings ('T' and &amp;quot;j&amp;quot;) the order of pen strokes is ambiguous. Even so, with the many parallels between on-line writing and speech, speech recognition methods should be applicable to on-line handwriting recognition.</Paragraph>
    <Paragraph position="3"> Since hidden Markov models currently constitute the state of the art in speech recognition, this method also seems a likel3~ candidate for handwriting recognition.</Paragraph>
    <Paragraph position="4"> There has been some interest in the use of HMMs for on-line handwriting recognition (see, for example, \[2, 3\]). However, the few studies that have used HMMs have dealt with small vocabularies, isolated characters, or isolated words. In this study, our objective is to deal with continuous cursive handwriting and large vocabularies (thousands of words) using a speech recognition system and language models.</Paragraph>
  </Section>
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