A System for Indian Postal Automation
Identifieur interne : 004224 ( Crin/Corpus ); précédent : 004223; suivant : 004225A System for Indian Postal Automation
Auteurs : Kaushik Roy ; Szilàrd Vajda ; Umapada Pal ; Bidyut Baran Chaudhuri ; Abdel BelaidSource :
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Abstract
In this paper, we present a system towards Indian postal automation based on pin-code and city name information. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and destination address block (DAB) is identified from the postal document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country that was earlier colonized by UK, the address part may be written by combination of two scripts : Latin (English) and a local (state) script. Here we shall consider Bangla script (local state language) with English for recognition. It is very difficult to identify the script by which the pin-code portion is written. So we have used two-stage artificial neural network based general classifiers for the recognition of pin-code digits written in English/Bangla. To identify the script by which a word/city name is written, we propose a water reservoir based technique. Based on script identification result the city names on the corresponding script will be recognized. For recognition of city names we propose a NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the digit recognition module is 93.14% while that of city name recognition scheme is about 86.44%
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" wicri:score="259">A System for Indian Postal Automation</title></titleStmt><publicationStmt><idno type="RBID">CRIN:roy0a</idno><date when="2005" year="2005">2005</date><idno type="wicri:Area/Crin/Corpus">004224</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A System for Indian Postal Automation</title><author><name sortKey="Roy, Kaushik" sort="Roy, Kaushik" uniqKey="Roy K" first="Kaushik" last="Roy">Kaushik Roy</name></author><author><name sortKey="Vajda, Szilard" sort="Vajda, Szilard" uniqKey="Vajda S" first="Szilàrd" last="Vajda">Szilàrd Vajda</name></author><author><name sortKey="Pal, Umapada" sort="Pal, Umapada" uniqKey="Pal U" first="Umapada" last="Pal">Umapada Pal</name></author><author><name sortKey="Chaudhuri, Bidyut Baran" sort="Chaudhuri, Bidyut Baran" uniqKey="Chaudhuri B" first="Bidyut Baran" last="Chaudhuri">Bidyut Baran Chaudhuri</name></author><author><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaid">Abdel Belaid</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>handwriting recognition</term><term>neural networks</term><term>postal automation</term><term>stochastic models</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="4787">In this paper, we present a system towards Indian postal automation based on pin-code and city name information. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and destination address block (DAB) is identified from the postal document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country that was earlier colonized by UK, the address part may be written by combination of two scripts : Latin (English) and a local (state) script. Here we shall consider Bangla script (local state language) with English for recognition. It is very difficult to identify the script by which the pin-code portion is written. So we have used two-stage artificial neural network based general classifiers for the recognition of pin-code digits written in English/Bangla. To identify the script by which a word/city name is written, we propose a water reservoir based technique. Based on script identification result the city names on the corresponding script will be recognized. For recognition of city names we propose a NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the digit recognition module is 93.14% while that of city name recognition scheme is about 86.44%</div></front></TEI><BibTex type="inproceedings"><ref>roy0a</ref><crinnumber>A05-R-214</crinnumber><category>3</category><equipe>CVPR Unit</equipe><author><e>Roy, Kaushik</e><e>Vajda, Szilàrd</e><e>Pal, Umapada</e><e>Chaudhuri, Bidyut Baran</e><e>Belaid, Abdel</e></author><title>A System for Indian Postal Automation</title><booktitle>{International Workshop on Document Analysis, Kolkata, India}</booktitle><year>2005</year><editor>Umapada Pal, Swapan K. Parui, Bidyut . Chaudhuri</editor><month>Mar</month><organization>Umapada Pal</organization><publisher>Allied Publishers Pvt. Ltd</publisher><keywords><e>neural networks</e><e>stochastic models</e><e>handwriting recognition</e><e>postal automation</e></keywords><abstract>In this paper, we present a system towards Indian postal automation based on pin-code and city name information. In the proposed system, at first, non-text blocks (postal stamp, postal seal etc.) are detected and destination address block (DAB) is identified from the postal document. Next, lines and words of the DAB are segmented. Since India is a multi-lingual and multi-script country that was earlier colonized by UK, the address part may be written by combination of two scripts : Latin (English) and a local (state) script. Here we shall consider Bangla script (local state language) with English for recognition. It is very difficult to identify the script by which the pin-code portion is written. So we have used two-stage artificial neural network based general classifiers for the recognition of pin-code digits written in English/Bangla. To identify the script by which a word/city name is written, we propose a water reservoir based technique. Based on script identification result the city names on the corresponding script will be recognized. For recognition of city names we propose a NSHP-HMM (Non-Symmetric Half Plane-Hidden Markov Model) based technique. At present, the accuracy of the digit recognition module is 93.14% while that of city name recognition scheme is about 86.44%</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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