A Language Model for Improving the Graph-Based Transcription Approach for Historical Documents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of a Language Model along with a dictionary into a graph-based recognizer, which aims at transcribing handwritten historical documents.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
收藏
页码：229 / 241
页数：13
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