Bank Cheque Data Mining: Integrated Cheque Recognition Technologies

Gorski, Nikolai

doi:10.1007/978-1-84628-726-8_20

Cited by 2 publications

(1 citation statement)

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“…In real applications, digit recognition is only one part of the complete recognition process, amongst document layout analysis, orientation correction, skew and slant correction, field segmentation, rejection of non-characters, etc. It is well known that these steps must be optimized all together in order to build a complete industrial application such as a bank check recognizer [4].…”

Section: Introductionmentioning

confidence: 99%

From Isolated Handwritten Characters to Fields Recognition: There's Many a Slip Twixt Cup and Lip

Kermorvant¹,

Bianne

Marty³

et al. 2009

2009 10th International Conference on Document Analysis and Recognition

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Recognition of handwritten characters has been a popular task for the evaluation of classification algorithms for many years. Looking at the latest results on databases such as USPS or MNIST, one could think that character recognition is a solved problem. In this paper, we claim that this is not the case for two reasons : first because the classical databases for digit recognition are realistic but too simple and second because digit recognition is not a real-world task but only a part of it. In this paper, we contribute to a better understanding of these two aspects with new results. In a first part, we compare three state-of-the-art recognizers on a digit recognition task extracted from a real world application and show that the error rates on this database can not be extrapolated from MNIST. Then, in a second part, we present and evaluate a system designed for an industrial application based on character recognition : document identification with floating field recognition.

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Section: Introductionmentioning

confidence: 99%