An automatic reading system for handwritten numeral amounts on French checks

Lethelier, E.; Leroux, Manuel; Gilloux, M.

doi:10.1109/icdar.1995.598951

Cited by 39 publications

(24 citation statements)

References 14 publications

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“…The structure selected for this module is the multilayer perceptron (MLP), which is the most widely used type of network for character recognition [49]. Other researchers have employed radial basis function networks (RBFN) and time delay neural networks (TDNN) for character recognition in French checks [41] and have attained similar results [50].…”

Section: Neural Network Based Recognitionmentioning

confidence: 99%

“…Researchers have also described or implemented systems to read courtesy amount in checks [27,30], and some of these systems are geared to a particular writing language; for example [29] has been developed for Korean checks, [37,41] for checks written in French, and [1,28] for U.S. checks. Further, some check processing systems focus on reading the legal amount [26]; see [2] for Brazilian checks, [35] for English language ones, and [37,22,23] for French and English ones.…”

Section: Introductionmentioning

confidence: 99%

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Reading Courtesy Amounts on Handwritten Paper Checks

2002

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In spite of the rapid advances in computers and communication technologies, a number of large-scale applications continue to rely heavily on the use of paper as the dominant medium, either in an intra-organizational or an inter-organizational environment. One major example of this category of paper intensive applications is the check processing application. In a number of countries, the value of each check is read by human eyes before the check is physically transported, in stages, from the point it was presented to the location of the branch of the bank which issued the blank check to the concerned account holder. This process of manual reading of each check involves significant time and cost. In this paper, a new approach is proposed to read the numerical amount field on the check; this field is also called the courtesy amount field. In the case of check processing, the segmentation of unconstrained strings into individual digits is a challenging task because one needs to accommodate special cases involving: connected or overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a neighboring digit. The system described in this paper involves three stages: the segmentation of the string into a series of individual characters; the normalization of each isolated character; and the recognition of each character based on a neural network classifier.

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Section: Neural Network Based Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reading Courtesy Amounts on Handwritten Paper Checks

2002

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“…The great bulk of them are still processed manually by human operators, the most common and labor-consuming operation being document amount reading and typing. Automation of bank check processing is an important and promising application of document recognition techniques [2,3,[12][13][14]22]. It uses both recent theoretical achievements of pattern recognition and document analysis [5,6,10,15,16,24,28,30], and practical approaches developed in adjacent application areas, such as postal automation or form recognition [3,4,8,25,29].…”

Section: Introductionmentioning

confidence: 99%

Industrial bank check processing: the A2iA CheckReaderTM

Gorski¹,

Anisimov²,

Augustin³

et al. 2001

IJDAR

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This paper presents the current state of the A2iA CheckReader TM -a commercial bank check recognition system. The system is designed to process the flow of payment documents associated with the check clearing process: checks themselves, deposit slips, money orders, cash tickets, etc. It processes document images and recognizes document amounts whatever their style and type -cursive, hand-or machine printed -expressed as numerals or as phrases. The system is adapted to read payment documents issued in different English-or Frenchspeaking countries. It is currently in use at more than 100 large sites in five countries and processes daily over 10 million documents. The average read rate at the document level varies from 65 to 85% with a misread rate corresponding to that of a human operator (1%).

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“…Other researchers have successfully employed radial basis function networks (RBFN) and time delay neural networks (TDNN) for character recognition in French checks. 11 In our endeavor, the recognition module is implemented as an array of four neural networks working in parallel. The results produced by these networks are analyzed by an arbiter function that evaluates the results of all the networks and then produces an overall result.…”

Section: Neural Network Based Recognitionmentioning

confidence: 99%

“…Other researchers have previously described or implemented systems to read courtesy amount, legal amount and date fields on checks. 6,7,8,3,9,10,11,12 This illustrates the broad, almost universal, interest in the area of automatic reading of bank checks.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Bank Check Recognition of Courtesy Amounts

Hielscher

Gupta

Wang

2004

Int. J. Image Grap.

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In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages: segmentation of the string into individual digits, normalization, recognition of each character using a neural network classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture that incorporates multiple approaches to provide high recognition rates.Keywords: Handwritten checks; Reading of unconstrained handwritten material; neural network based reading; automation of banking systems.

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An automatic reading system for handwritten numeral amounts on French checks

Cited by 39 publications

References 14 publications

Reading Courtesy Amounts on Handwritten Paper Checks

Reading Courtesy Amounts on Handwritten Paper Checks

Industrial bank check processing: the A2iA CheckReaderTM

Handwritten Bank Check Recognition of Courtesy Amounts

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