Neural Networks for Handwriting Recognition - Computational Intelligence Paradigms in Advanced Pattern Classification - page 9

The online and offline databases used are the IAM-OnDB [31] and the IAM-
DB [32] respectively. Note that these do not correspond to the same handwriting
samples: the IAM-OnDB was acquired from a whiteboard, while the IAM-DB
consists of scanned images of handwritten forms. 1
2.1 General Processing Steps
A recognition system for unconstrained Roman script is usually divided into con-
secutive units which iteratively process the handwritten input data to finally obtain
the transcription. The main units are illustrated in Fig. 1 and summarized in this
section. Certainly, there are differences between offline and online processing, but
the principles are the same. Only the methodology for performing the individual
steps differs.
First, preprocessing steps are applied to reduce noise in the raw data. The input
is raw handwritten data and the output usually consists of extracted text lines. The
amount of effort that need to be invested into the preprocessing depends on the
given data. If the data have been acquired from a system that does not produce any
noise and only single words have been recorded, there is nothing to do in this step.
But usually the data contains noise which need to be removed to improve the qual-
ity of the handwriting, e.g., by means of image enhancement. The offline images
are furthermore binarized and the online data, which usually contain noisy points
and gaps within strokes, is processed with some heuristics to recover from these
artifacts. These operations are described in Ref. [33]. The cleaned text data is then
automatically divided into lines using some simple heuristics.
Next, the data is normalized, i.e., it is attempted to remove writer-specific cha-
racteristics of the handwriting to make writings from different authors looking
more similar to each other. This is a very important step in any handwriting rec-
ognition system, because the writing styles of the writers differ with respect to
skew, slant, height, and width of the characters. In the literature there is no stan-
dard way of normalizing the data, but many systems use similar techniques. First,
the text line is corrected in regard to its skew, i.e., it is rotated, so that the baseline
is parallel to the x-axis. Then, slant correction is performed so that the slant be-
comes upright. The next important step is the computation of the baseline and the
corpus line. These two lines divide the text into three areas: the upper area, which
mainly contains the ascenders of the letters; the middle area, where the corpus of
the letters is present; and the lower area with the descenders of some letters. These
three areas are normalized to predefined heights. Often, some additional normali-
zation steps are performed, depending on the domain. In offline recognition, thin-
ning and binarization may be applied. In online recognition the delayed strokes,
e.g., the crossing of a “t” or the dot of an “i”, are usually removed, and equidistant
resampling is applied.
1 The databases and benchmark tasks are available on
http://www.iam.unibe.ch/fki/databases