Multi-modal segmental models for on-line handwriting recognition

被引：0

作者：

Artières, T ^{[1
]}

Marchand, JM ^{[1
]}

Dorizzi, B ^{[1
]}

机构：

[1] LIP6, Paris, France

来源：

15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Hidden Markov Models (HMMs) have become within a few years the main technology for on line handwritten word recognition (HWR). We consider here segment models which generalize HMMs, these models aim at modeling the signal at a global level rather than at the frame level and have been shown to overcome standard HMMs in their modeling ability. We propose a new segment model which allows to automatically handle different writing styles. We compare our system on the isolated character set of the UNIPEN database with a reference system and a baseline segment model.

引用

页码：247 / 250

页数：4

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