Bibtex entry:

@inproceedings{icdar95:Kuh,
   key = {icdar95:Kuh},
   keywords = {ocr, form processing, classifiers, algorithms, neural networks},
   author = {K. Kuhnke and L. Simoncini and Zs. M. Kovacs-V.},
   title = {A System for Machine-Written and Hand-Written Character
            Distinction},
   booktitle = {Proc. of ICDAR'95 Montreal - Canada},
   volume = {2},
   pages = {811--814},
   month = {Aug},
   year = {1995},
   abstract={
In applications of character recognition where machine-printed and
hand-written characters are involved, it is important to know if the
character image, or the whole word, is machine- or hand-written. This
is due to the accuracy difference between the algorithms and systems
oriented to machine- or hand-written characters. Obviously, this type of
knowledge leads to the increase of the overall system quality. In this
work a classification system is presented which reads a raster image of
a character and outputs two confidence values, one for ``machine-written''
and one for ``hand-written'' character classes, respectively. The
proposed system features a preprocessing step, which transforms a general
uncentered character image into a normalized form, then the feature
extraction phase extracts relevant information from the image, and at the
end, a standard classifier based on a feed-forward neural network creates
the final response. At the end, some results on a proprietary image
database are reported.
},
}

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