Multi-lingual City Name Recognition for Indian Postal Automation

Under three-language formula, the destination address block of postal document of an Indian state is generally written in three languages: English, Hindi and the State official language. From the statistical analysis we found that 12.37%, 76.32% and 10.21% postal documents are written in Bangla, Eng...

Full description

Saved in:
Bibliographic Details
Published in2012 International Conference on Frontiers in Handwriting Recognition pp. 169 - 173
Main Authors Pal, U., Roy, R. K., Kimura, F.
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.09.2012
Subjects
Automation
Cavity resonators
Cities and towns
city name recognition
Dynamic programming
Feature extraction
Handwriting recognition
Handwritten character recognition
Image segmentation
Indian postal automation
Indian Script
Online AccessGet full text
ISBN9781467322621
1467322628
DOI10.1109/ICFHR.2012.238

Cover

More Information
Summary:Under three-language formula, the destination address block of postal document of an Indian state is generally written in three languages: English, Hindi and the State official language. From the statistical analysis we found that 12.37%, 76.32% and 10.21% postal documents are written in Bangla, English and Devanagari script, respectively. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a city name is written. To avoid such script identification difficulties, in this paper we proposed a lexicon-driven method for multi-lingual (English, Hindi and Bangla) city name recognition for Indian postal automation. In the proposed scheme, at first, to take care of slanted handwriting of different individuals a slant correction technique is performed. Next, a water reservoir concept is applied to pre-segment the slant corrected city names into possible primitive components (characters or its parts). Pre-segmented components of a city name are then merged into possible characters to get the best city name using the lexicon information. In order to merge these primitive components into characters and to find optimum character segmentation, dynamic programming (DP) is applied using total likelihood of the characters of a city name as an objective function. We tested our system on 16132 Indian trilingual city names and 92.25% overall recognition accuracy was obtained.
ISBN:9781467322621
1467322628
DOI:10.1109/ICFHR.2012.238