Bank check reading: Recognizing the courtesy amount
Identifieur interne : 002B34 ( Main/Exploration ); précédent : 002B33; suivant : 002B35Bank check reading: Recognizing the courtesy amount
Auteurs : Valeri Anisimov [Russie] ; Nikolai Gorski [Russie, France] ; David Price [France] ; Olivier Baret [France] ; Stefan Knerr [France]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1995.
Abstract
Abstract: We developed a check reading system which recognizes both the legal amount and the courtesy amount on bank checks. It addresses the problem of French, omni-scriptor, cursive handwriting recognition, and is designed to meet industrial requirements, such as high processing speed, robustness, and extremely low error rates. Our system is based on several key ideas: (1) hierarchical organization; starting out with pixel images, the system elaborates intermediate representations, such as strokes, letters, and words, which are grouped to form objects in higher levels. (2) The objects of any hierarchical level are described in terms of soft decisions (probabilities). Hard decisions are only taken in the final amount recognition process. (3) Use of prior information when available. (4) Wherever possible, our system makes use of several complementary algorithms to accomplish a given task. This paper deals particularly with the recognition of the courtesy (numeral) amount. Results obtained on a large data base of French bank checks are presented.
Url:
DOI: 10.1007/3-540-60697-1_99
Affiliations:
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It addresses the problem of French, omni-scriptor, cursive handwriting recognition, and is designed to meet industrial requirements, such as high processing speed, robustness, and extremely low error rates. Our system is based on several key ideas: (1) hierarchical organization; starting out with pixel images, the system elaborates intermediate representations, such as strokes, letters, and words, which are grouped to form objects in higher levels. (2) The objects of any hierarchical level are described in terms of soft decisions (probabilities). Hard decisions are only taken in the final amount recognition process. (3) Use of prior information when available. (4) Wherever possible, our system makes use of several complementary algorithms to accomplish a given task. This paper deals particularly with the recognition of the courtesy (numeral) amount. Results obtained on a large data base of French bank checks are presented.</div></front></TEI><affiliations><list><country><li>France</li><li>Russie</li></country><region><li>Île-de-France</li></region><settlement><li>Paris</li></settlement></list><tree><country name="Russie"><noRegion><name sortKey="Anisimov, Valeri" sort="Anisimov, Valeri" uniqKey="Anisimov V" first="Valeri" last="Anisimov">Valeri Anisimov</name></noRegion><name sortKey="Gorski, Nikolai" sort="Gorski, Nikolai" uniqKey="Gorski N" first="Nikolai" last="Gorski">Nikolai Gorski</name></country><country name="France"><region name="Île-de-France"><name sortKey="Gorski, Nikolai" sort="Gorski, Nikolai" uniqKey="Gorski N" first="Nikolai" last="Gorski">Nikolai Gorski</name></region><name sortKey="Baret, Olivier" sort="Baret, Olivier" uniqKey="Baret O" first="Olivier" last="Baret">Olivier Baret</name><name sortKey="Gorski, Nikolai" sort="Gorski, Nikolai" uniqKey="Gorski N" first="Nikolai" last="Gorski">Nikolai Gorski</name><name sortKey="Knerr, Stefan" sort="Knerr, Stefan" uniqKey="Knerr S" first="Stefan" last="Knerr">Stefan Knerr</name><name sortKey="Price, David" sort="Price, David" uniqKey="Price D" first="David" last="Price">David Price</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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