DocumentCode :
1908393
Title :
Multiresolution neural networks for omnifont character recognition
Author :
Wang, Jin ; Jean, Jack
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
fYear :
1993
fDate :
1993
Firstpage :
1588
Abstract :
A multiresolution optical character recognition (OCR) using neural networks is proposed for omnifont character recognition. It is motivated by the human reading process in which a low resolution is used to effectively process the majority of clean and unambiguous text, while a more complicated recognition scheme is invoked only when a high resolution is needed. Compared with the method that utilizes single resolution, the multiresolution system not only speeds up recognition by up to 20 times, but also improves accuracy of isolated character recognition from 99.8% to 99.9%. The multiresolution approach captures the essence of better reading, and provides the building blocks for the next-generation OCR systems
Keywords :
neural nets; optical character recognition; OCR; multiresolution optical character recognition; neural networks; omnifont character recognition; Acoustic noise; Character recognition; Computer science; Humans; Lifting equipment; Neural networks; Optical character recognition software; Signal resolution; Switches; Text recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
Type :
conf
DOI :
10.1109/ICNN.1993.298793
Filename :
298793
Link To Document :
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