Abstract
In this paper, we present a hybrid online handwriting recognition system based on hidden Markov models (HMMs). It is devoted to word recognition using large vocabularies. An adaptive segmentation of words into letters is integrated with recognition, and is at the heart of the training phase. A word-model is a left-right HMM in which each state is a predictive multilayer perceptron that performs local regression on the drawing (i.e., the written word) relying on a context of observations. A discriminative training paradigm related to maximum mutual information is used, and its potential is shown on a database of 9,781 words.
| Original language | English |
|---|---|
| Pages (from-to) | 56-68 |
| Number of pages | 13 |
| Journal | International Journal on Document Analysis and Recognition |
| Volume | 4 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Dec 2001 |
| Externally published | Yes |
Keywords
- Dynamic segmentation
- Handwritten word recognition
- Hidden Markov model
- Maximum mutual information
- Predictive neural network
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