DocumentCode
1638929
Title
Language Model Integration for the Recognition of Handwritten Medieval Documents
Author
Wuthrich, Manuel ; Liwicki, Marcus ; Fischer, Andreas ; Indermuhle, E. ; Bunke, Horst ; Viehhauser, Gabriel ; Stolz, Michael
Author_Institution
Inst. of Comput. Sci. & Appl. Math., Univ. of Bern, Bern, Switzerland
fYear
2009
Firstpage
211
Lastpage
215
Abstract
Building recognition systems for historical documents is a difficult task. Especially, when it comes to medieval scripts. The complexity is mainly affected by the poor quality and the small quantity of the data available. In this paper we apply an HMM based recognition system to medieval manuscripts from the 13th century written in Middle High German. The recognition system, which was originally developed for modern scripts, has been adapted to medieval scripts. Beside the data processing, one of the major challenges is to create a suitable language model. Because of the lack of appropriate independent text corpora for medieval languages, the language model has to be created on the base of a rather small number of manuscripts only. Due to the small size of the corpus, optimizing the language model parameters can quickly lead to the problem of overfitting. In this paper we describe a strategy to integrate all available information into the language model and to optimize the language model parameters without suffering from this problem.
Keywords
handwriting recognition; handwritten character recognition; hidden Markov models; history; text analysis; HMM based recognition system; handwritten medieval documents recognition; historical documents recognition systems; independent text corpora; language model integration; language model parameters; medieval languages; medieval manuscripts; Computer science; Data processing; Digital images; Handwriting recognition; Hidden Markov models; Mathematics; Natural languages; Software libraries; Text analysis; Writing; HMM; Historical Documents; Language Model; Overfitting;
fLanguage
English
Publisher
ieee
Conference_Titel
Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on
Conference_Location
Barcelona
ISSN
1520-5363
Print_ISBN
978-1-4244-4500-4
Electronic_ISBN
1520-5363
Type
conf
DOI
10.1109/ICDAR.2009.17
Filename
5277727
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