Title :
Position Invariant Optical Character Recognition through Symmetry Features
Author :
Holland, Sam ; Neville, Richard
Author_Institution :
Machine Learning & Optimisation Res. Group, Univ. of Manchester, Manchester, UK
Abstract :
We propose an effective method to achieve position invariance in the application of optical character recognition (OCR). We normalise the position of all inputs based on their symmetry features. The generalized symmetry transform (GST) is used to determine the symmetry features prior to classification by a probabilistic neural network (PNN). We used the United States Postal Service (USPS) data set to measure performance.
Keywords :
character recognition; neural nets; transforms; United States Postal Service data set; generalized symmetry transform; position invariant optical character recognition; probabilistic neural network; symmetry features; Character recognition; Neural networks; Neurons; Optical character recognition software; Optical computing; Optical control; Optical network units; Optical reflection; Telecommunication computing; Telecommunication control; character; generalized; invariance; network; neural; position; probabilistic; recognition; symmetry; transform;
Conference_Titel :
Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on
Conference_Location :
Trivandrum, Kerala
Print_ISBN :
978-1-4244-5321-4
Electronic_ISBN :
978-0-7695-3915-7
DOI :
10.1109/ACT.2009.84
