Paper Info
Title
Enhancing the Mongolian Historical Document Recognition System with Multiple Knowledge-Based Strategies.
Abstract
This paper describes recent work on integrating multiple strategies to improve the performance of the Mongolian historical document recognition system which utilize the segmentation-based scheme. We analyze the reasons why the recognition errors happened. On such basis, we propose three strategies according to the knowledge of the glyph characteristics of Mongolian and integrate them into glyph-unit recognition. The strategies are recognizing the under-segmented and over-segmented fragments RUOF, glyph-unit grouping GG and incorporating the baseline information IBI. The first strategy helps in correcting the segmentation error and the remaining two strategies further improve the classifiers accuracies. The experiment on the historical Mongolian Kanjur demonstrates that utilizing these strategies could effectively increase the accuracy of word recognition.
Year
DOI
Venue
2015
10.1007/978-3-319-26535-3_61
ICONIP
Keywords
Field
DocType
Mongolian,Historical document,Knowledge-based strategy,Glyph-unit recognition
Glyph,Recognition system,Segmentation,Computer science,Word recognition,Artificial intelligence,Machine learning,Historical document
Conference
Volume
ISSN
Citations 
9490
0302-9743
0
PageRank 
References 
Authors
0.34
7
4
Name
Order
Citations
PageRank
Xiangdong Su1184.68
Guanglai Gao27824.57
Hongxi Wei300.34
Fei Long41613.09