A Language Model for Improving the Graph-Based Transcription Approach for Historical Documents

被引：0

作者：

Lecireth Meza-Lovon, Graciela ^{[1
]}

机构：

[1] Univ La Salle, Arequipa, Peru

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE (IBERAMIA 2014) | 2014年 / 8864卷

关键词：

Language model; Bigram; Dictionary; Text transcription; Handwriting recognition; Support vector machines; HANDWRITING RECOGNITION;

D O I：

10.1007/978-3-319-12027-0_19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Language Models (LMs) capture the contextual dependencies of a language and assign higher probabilities to well-formed sequences of words. For that reason, LMs have been commonly used in generic handwriting recognition, improving recognition results. In this paper, we present the integration of a Language Model along with a dictionary into a graph-based recognizer, which aims at transcribing handwritten historical documents. The results of such integration show a significant improvement on word accuracy when applied to our corpora.

引用

页码：229 / 241

页数：13

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