MICR Automated Recognition based on Paraconsistent Artificial Neural Networks | Zendy

Sheila Souza | Zendy; Jair Minoro Abe | Zendy; Kazumi Nakamatsu | Zendy

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MICR Automated Recognition based on Paraconsistent Artificial Neural Networks

Author(s) -

Sheila Souza,

Jair Minoro Abe,

Kazumi Nakamatsu

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.09.194

Subject(s) - computer science , artificial intelligence , artificial neural network , machine learning , pattern recognition (psychology)

The purpose of this paper is to discuss an automated computational system able to recognize MICR characters commonly used on bank checks based on Paraconsistent Artificial Neural Networks due to their intrinsic ability to deal with imprecise, inconsistent and paracomplete data. The recognition process is carried out from character features chosen in advance based on Graphology and Graphoscopy techniques. The analysis of such features and the character recognition are performed employing Paraconsistent Artificial Neural Networks. Actual checks batches were presented to validate the proposed study and 97.8 percent of the characters were recognized correctly by the system

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