DocumentCode :
3141134
Title :
A new approach for multilingual address recognition
Author :
Aoki, Y. ; Akagi, T. ; Nakao, A. ; Natori, N. ; Mizutani, H.
Author_Institution :
Toshiba Corp., Japan
fYear :
1999
fDate :
20-22 Sep 1999
Firstpage :
313
Lastpage :
316
Abstract :
In this paper, we proposed, a new method, for multilingual address recognition which is based, on MAP (maximum a posteriori probability) estimation. In the method, an input document image is decomposed to fundamental factors, which are characters, words, text lines and, address areas. These factors are constructed hierarchically in accordance with the degree of their abstraction. As a statistical method is applied, to the formulated relationships among the factors, recognition of a document image is essentially equal to finding the proper position of factors, and classifying each character is attained, through a learning process involving samples of any language type, and thus proper weight coefficients are found, without heuristic management. We constructed, a trial recognition system based on the proposed method. Experimental results show that the proposed system is very promising not only for Japanese envelopes but also for English ones. The proposed method, of address reading is applicable to any type of language, i.e., it is multilingual, by virtue of proper learning from samples
Keywords :
image recognition; maximum likelihood estimation; visual databases; Japanese envelopes; address areas; address reading; characters; document image recognition; fundamental factors; learning from samples; maximum a posteriori probability estimation; multilingual address recognition; text lines; trial recognition system; words; Bayesian methods; Character recognition; Design methodology; Image recognition; Natural languages; Statistical analysis; Text recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 1999. ICDAR '99. Proceedings of the Fifth International Conference on
Conference_Location :
Bangalore
Print_ISBN :
0-7695-0318-7
Type :
conf
DOI :
10.1109/ICDAR.1999.791787
Filename :
791787
Link To Document :
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