OPEN VOCABULARY HANDWRITING RECOGNITION USING COMBINED WORD-LEVEL AND CHARACTER-LEVEL LANGUAGE MODELS

被引：0

作者：

Kozielski, Michal ^{[1
]}

Rybach, David ^{[1
]}

Hahn, Stefan ^{[1
]}

Schlueter, Ralf ^{[1
]}

Ney, Hermann ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Dept Comp Sci, Aachen, Germany

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2013年

关键词：

open vocabulary recognition; handwriting recognition; character-based language models; NORMALIZATION; COMBINATION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this paper, we present a unified search strategy for open vocabulary handwriting recognition using weighted finite state transducers. Additionally to a standard word-level language model we introduce a separate n-gram character-level language model for out-of-vocabulary word detection and recognition. The probabilities assigned by those two models are combined into one Bayes decision rule. We evaluate the proposed method on the IAM database of English handwriting. An improvement from 22.2% word error rate to 1 7.3 % is achieved comparing to the closed-vocabulary scenario and the best published result.

引用

页码：8257 / 8261

页数：5

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